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v100/yang20a | Data Efficient Reinforcement Learning for Legged Robots | https://proceedings.mlr.press/v100/yang20a.html | [
"Yuxiang Yang",
"Ken Caluwaerts",
"Atil Iscen",
"Tingnan Zhang",
"Jie Tan",
"Vikas Sindhwani"
] | null | null | We present a model-based reinforcement learning framework for robot locomotion that achieves walking based on only 4.5 minutes of data collected on a quadruped robot. To accurately model the robot’s dynamics over a long horizon, we introduce a loss function that tracks the model’s prediction over multiple timesteps. We... | [] | null | null | 1907.03613 | title_snapshot |
v100/lee20a | To Follow or not to Follow: Selective Imitation Learning from Observations | https://proceedings.mlr.press/v100/lee20a.html | [
"Youngwoon Lee",
"Edward S. Hu",
"Zhengyu Yang",
"Joseph J. Lim"
] | null | null | Learning from demonstrations is a useful way to transfer a skill from one agent to another. While most imitation learning methods aim to mimic an expert skill by following the demonstration step-by-step, imitating every step in the demonstration often becomes infeasible when the learner and its environment are differen... | [] | null | null | 1912.07670 | title_snapshot |
v100/balakrishna20a | On-Policy Robot Imitation Learning from a Converging Supervisor | https://proceedings.mlr.press/v100/balakrishna20a.html | [
"Ashwin Balakrishna",
"Brijen Thananjeyan",
"Jonathan Lee",
"Felix Li",
"Arsh Zahed",
"Joseph E. Gonzalez",
"Ken Goldberg"
] | null | null | Existing on-policy imitation learning algorithms, such as DAgger, assume access to a fixed supervisor. However, there are many settings where the supervisor may evolve during policy learning, such as a human performing a novel task or an improving algorithmic controller. We formalize imitation learning from a “convergi... | [] | null | null | 1907.03423 | title_snapshot |
v100/fang20a | Dynamics Learning with Cascaded Variational Inference for Multi-Step Manipulation | https://proceedings.mlr.press/v100/fang20a.html | [
"Kuan Fang",
"Yuke Zhu",
"Animesh Garg",
"Silvio Savarese",
"Li Fei-Fei"
] | null | null | The fundamental challenge of planning for multi-step manipulation is to find effective and plausible action sequences that lead to the task goal. We present Cascaded Variational Inference Planner (CAVIN), a model-based method that hierarchically generates plans by sampling from latent spaces. To facilitate planning ove... | [] | null | null | 1910.13395 | title_snapshot |
v100/qin20a | S4G: Amodal Single-view Single-Shot SE(3) Grasp Detection in Cluttered Scenes | https://proceedings.mlr.press/v100/qin20a.html | [
"Yuzhe Qin",
"Rui Chen",
"Hao Zhu",
"Meng Song",
"Jing Xu",
"Hao Su"
] | null | null | Grasping is among the most fundamental and long-lasting problems in robotics study. This paper studies the problem of 6-DoF(degree of freedom) grasping by a parallel gripper in a cluttered scene captured using a commodity depth sensor from a single viewpoint. We address the problem in a learning-based framework. At the... | [] | null | null | 1910.14218 | title_snapshot |
v100/chen20a | Learning by Cheating | https://proceedings.mlr.press/v100/chen20a.html | [
"Dian Chen",
"Brady Zhou",
"Vladlen Koltun",
"Philipp Krähenbühl"
] | null | null | Vision-based urban driving is hard. The autonomous system needs to learn to perceive the world and act in it. We show that this challenging learning problem can be simplified by decomposing it into two stages. We first train an agent that has access to privileged information. This privileged agent cheats by observing t... | [] | null | null | 1912.12294 | title_snapshot |
v100/magassouba20a | Multimodal Attention Branch Network for Perspective-Free Sentence Generation | https://proceedings.mlr.press/v100/magassouba20a.html | [
"Aly Magassouba",
"Komei Sugiura",
"Hisashi Kawai"
] | null | null | In this paper, we address the automatic sentence generation of fetching instructions for domestic service robots. Typical fetching commands such as “bring me the yellow toy from the upper part of the white shelf” includes referring expressions, i.e., “from the white upper part of the white shelf”. To solve this task, w... | [] | null | null | 1909.05664 | title_snapshot |
v100/chai20a | MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction | https://proceedings.mlr.press/v100/chai20a.html | [
"Yuning Chai",
"Benjamin Sapp",
"Mayank Bansal",
"Dragomir Anguelov"
] | null | null | Predicting human behavior is a difficult and crucial task required for motion planning. It is challenging in large part due to the highly uncertain and multimodal set of possible outcomes in real-world domains such as autonomous driving. Beyond single MAP trajectory prediction [1, 2], obtaining an accurate probability ... | [] | null | null | 1910.05449 | title_snapshot |
v100/ye20a | Object-centric Forward Modeling for Model Predictive Control | https://proceedings.mlr.press/v100/ye20a.html | [
"Yufei Ye",
"Dhiraj Gandhi",
"Abhinav Gupta",
"Shubham Tulsiani"
] | null | null | We present an approach to learn an object-centric forward model, and show that this allows us to plan for sequences of actions to achieve distant desired goals. We propose to model a scene as a collection of objects, each with an explicit spatial location and implicit visual feature, and learn to model the effects of a... | [] | null | null | 1910.03568 | title_snapshot |
v100/nachum20a | Multi-Agent Manipulation via Locomotion using Hierarchical Sim2Real | https://proceedings.mlr.press/v100/nachum20a.html | [
"Ofir Nachum",
"Michael Ahn",
"Hugo Ponte",
"Shixiang (Shane) Gu",
"Vikash Kumar"
] | null | null | Manipulation and locomotion are closely related problems that are often studied in isolation. In this work, we study the problem of coordinating multiple mobile agents to exhibit manipulation behaviors using a reinforcement learning (RL) approach. Our method hinges on the use of hierarchical sim2real – a simulated envi... | [] | null | null | 1908.05224 | title_snapshot |
v100/ancha20a | Combining Deep Learning and Verification for Precise Object Instance Detection | https://proceedings.mlr.press/v100/ancha20a.html | [
"Siddharth Ancha",
"Junyu Nan",
"David Held"
] | null | null | Deep learning based object detectors often report false positives with very high confidence. Although they optimize generic detection performance, such as mean average precision (mAP), they are not designed for robustness or verifiability. We argue that, if a high confidence detection is made by a robot perception syst... | [] | null | null | 1912.12270 | title_snapshot |
v100/wu20a | MAT: Multi-Fingered Adaptive Tactile Grasping via Deep Reinforcement Learning | https://proceedings.mlr.press/v100/wu20a.html | [
"Bohan Wu",
"Iretiayo Akinola",
"Jacob Varley",
"Peter K. Allen"
] | null | null | Vision-based grasping systems typically adopt an open-loop execution of a planned grasp. This policy can fail due to many reasons, including ubiquitous calibration error. Recovery from a failed grasp is further complicated by visual occlusion, as the hand is usually occluding the vision sensor as it attempts another op... | [] | null | null | 1909.04787 | title_snapshot |
v100/bechtle20a | Curious iLQR: Resolving Uncertainty in Model-based RL | https://proceedings.mlr.press/v100/bechtle20a.html | [
"Sarah Bechtle",
"Yixin Lin",
"Akshara Rai",
"Ludovic Righetti",
"Franziska Meier"
] | null | null | Curiosity as a means to explore during reinforcement learning problems has recently become very popular. However, very little progress has been made in utilizing curiosity for learning control. In this work, we propose a model-based reinforcement learning (MBRL) framework that combines Bayesian modeling of the system d... | [] | null | null | 1904.06786 | title_snapshot |
v100/burke20a | Hybrid system identification using switching density networks | https://proceedings.mlr.press/v100/burke20a.html | [
"Michael Burke",
"Yordan Hristov",
"Subramanian Ramamoorthy"
] | null | null | Behaviour cloning is a commonly used strategy for imitation learning and can be extremely effective in constrained domains. However, in cases where the dynamics of an environment may be state dependent and varying, behaviour cloning places a burden on model capacity and the number of demonstrations required.This paper ... | [] | null | null | 1907.04360 | title_snapshot |
v100/boney20a | Regularizing Model-Based Planning with Energy-Based Models | https://proceedings.mlr.press/v100/boney20a.html | [
"Rinu Boney",
"Juho Kannala",
"Alexander Ilin"
] | null | null | Model-based reinforcement learning could enable sample-efficient learning by quickly acquiring rich knowledge about the world and using it to improve behaviour without additional data. Learned dynamics models can be directly used for planning actions but this has been challenging because of inaccuracies in the learned ... | [] | null | null | 1910.05527 | title_snapshot |
v100/unhelkar20a | Semi-Supervised Learning of Decision-Making Models for Human-Robot Collaboration | https://proceedings.mlr.press/v100/unhelkar20a.html | [
"Vaibhav V. Unhelkar",
"Shen Li",
"Julie A. Shah"
] | null | null | We consider human-robot collaboration in sequential tasks with known task objectives. For interaction planning in this setting, the utility of models for decision-making under uncertainty has been demonstrated across domains. However, in practice, specifying the model parameters remains challenging, requiring significa... | [] | null | null | null | null |
v100/mukadam20a | Riemannian Motion Policy Fusion through Learnable Lyapunov Function Reshaping | https://proceedings.mlr.press/v100/mukadam20a.html | [
"Mustafa Mukadam",
"Ching-An Cheng",
"Dieter Fox",
"Byron Boots",
"Nathan Ratliff"
] | null | null | RMPflow is a recently proposed policy-fusion framework based on differential geometry. While RMPflow has demonstrated promising performance, it requires the user to provide sensible subtask policies as Riemannian motion policies (RMPs: a motion policy and an importance matrix function), which can be a difficult design ... | [] | null | null | 1910.02646 | title_snapshot |
v100/lee20b | Perceptual Attention-based Predictive Control | https://proceedings.mlr.press/v100/lee20b.html | [
"Keuntaek Lee",
"Gabriel Nakajima An",
"Viacheslav Zakharov",
"Evangelos A. Theodorou"
] | null | null | In this paper, we present a novel information processing architecture for safe deep learning-based visual navigation of autonomous systems. The proposed information processing architecture is used to support a perceptual attention-based predictive control algorithm that leverages model predictive control (MPC), convolu... | [] | null | null | 1904.11898 | title_snapshot |
v100/jaquier20a | Bayesian Optimization Meets Riemannian Manifolds in Robot Learning | https://proceedings.mlr.press/v100/jaquier20a.html | [
"Noémie Jaquier",
"Leonel Rozo",
"Sylvain Calinon",
"Mathias Bürger"
] | null | null | Bayesian optimization (BO) recently became popular in robotics to optimize control parameters and parametric policies in direct reinforcement learning due to its data efficiency and gradient-free approach. However, its performance may be seriously compromised when the parameter space is high-dimensional. A way to tackl... | [] | null | null | 1910.04998 | title_snapshot |
v100/jaquier20b | Learning from demonstration with model-based Gaussian process | https://proceedings.mlr.press/v100/jaquier20b.html | [
"Noémie Jaquier",
"David Ginsbourger",
"Sylvain Calinon"
] | null | null | In learning from demonstrations, it is often desirable to adapt the behavior of the robot as a function of the variability retrieved from human demonstrations and the (un)certainty encoded in different parts of the task. In this paper, we propose a novel multi-output Gaussian process (MOGP) based on Gaussian mixture re... | [] | null | null | 1910.05005 | title_snapshot |
v100/okada20a | Variational Inference MPC for Bayesian Model-based Reinforcement Learning | https://proceedings.mlr.press/v100/okada20a.html | [
"Masashi Okada",
"Tadahiro Taniguchi"
] | null | null | In recent studies on model-based reinforcement learning (MBRL), incorporating uncertainty in forward dynamics is a state-of-the-art strategy to enhance learning performance, making MBRLs competitive to cutting-edge modelfree methods, especially in simulated robotics tasks. Probabilistic ensembles with trajectory sampli... | [] | null | null | 1907.04202 | title_snapshot |
v100/schwenkel20a | Optimizing Sequences of Probabilistic Manipulation Skills Learned from Demonstration | https://proceedings.mlr.press/v100/schwenkel20a.html | [
"Lukas Schwenkel",
"Meng Guo",
"Mathias Bürger"
] | null | null | While manipulation skills such as picking, inserting and placing were hard coded in classical setups, it is now widely understood that this leads to poor flexibility and that more general skill formulations are required to ensure re-usability in new scenarios. We thus adopt a skill-centric approach where each skill is ... | [] | null | null | null | null |
v100/guo20a | Predictive Safety Network for Resource-constrained Multi-agent Systems | https://proceedings.mlr.press/v100/guo20a.html | [
"Meng Guo",
"Mathias Bürger"
] | null | null | Coordinating multiple agents, such as mobile robots, with shared resources, such as common battery charging stations, is a highly relevant but still challenging decision problem. Traditionally, the motion and task planning of multi-agent systems are tackled by either designing ad-hoc decision rules or employing optimiz... | [] | null | null | null | null |
v100/koutras20a | A correct formulation for the Orientation Dynamic Movement Primitives for robot control in the Cartesian space | https://proceedings.mlr.press/v100/koutras20a.html | [
"Leonidas Koutras",
"Zoe Doulgeri"
] | null | null | Dynamic movement primitives (DMP) are an efficient way for learning and reproducing complex robot behaviors. A singularity free DMP formulation for orientation in the Cartesian space is proposed by Ude et al. [1] and has been largely adopted by the research community. In this work, we demonstrate the undesired oscillat... | [] | null | null | null | null |
v100/barnes20a | Masking by Moving: Learning Distraction-Free Radar Odometry from Pose Information | https://proceedings.mlr.press/v100/barnes20a.html | [
"Dan Barnes",
"Rob Weston",
"Ingmar Posner"
] | null | null | This paper presents an end-to-end radar odometry system which delivers robust, real-time pose estimates based on a learned embedding space free of sensing artefacts and distractor objects. The system deploys a fully differentiable, correlation-based radar matching approach. This provides the same level of interpretabil... | [] | null | null | 1909.03752 | title_snapshot |
v100/xie20a | Learning Locomotion Skills for Cassie: Iterative Design and Sim-to-Real | https://proceedings.mlr.press/v100/xie20a.html | [
"Zhaoming Xie",
"Patrick Clary",
"Jeremy Dao",
"Pedro Morais",
"Jonanthan Hurst",
"Michiel Panne"
] | null | null | Deep reinforcement learning (DRL) is a promising approach for developing legged locomotion skills. However, current work commonly describes DRL as being a one-shot process, where the state, action and reward are assumed to be well defined and are directly used by an RL algorithm to obtain policies. In this paper, we de... | [] | null | null | 1903.09537 | title_judge |
v100/brown20a | Better-than-Demonstrator Imitation Learning via Automatically-Ranked Demonstrations | https://proceedings.mlr.press/v100/brown20a.html | [
"Daniel S. Brown",
"Wonjoon Goo",
"Scott Niekum"
] | null | null | The performance of imitation learning is typically upper-bounded by the performance of the demonstrator. While recent empirical results demonstrate that ranked demonstrations allow for better-than-demonstrator performance, preferences over demonstrations may be difficult to obtain, and little is known theoretically abo... | [] | null | null | 1907.03976 | title_snapshot |
v100/leibfried20a | Mutual-Information Regularization in Markov Decision Processes and Actor-Critic Learning | https://proceedings.mlr.press/v100/leibfried20a.html | [
"Felix Leibfried",
"Jordi Grau-Moya"
] | null | null | Cumulative entropy regularization introduces a regulatory signal to the reinforcement learning (RL) problem that encourages policies with high-entropy actions, which is equivalent to enforcing small deviations from a uniform reference marginal policy. This has been shown to improve exploration and robustness, and it ta... | [] | null | null | 1909.05950 | title_snapshot |
v100/du20a | Model-Based Planning with Energy-Based Models | https://proceedings.mlr.press/v100/du20a.html | [
"Yilun Du",
"Toru Lin",
"Igor Mordatch"
] | null | null | Model-based planning holds great promise for improving both sample efficiency and generalization in reinforcement learning (RL). We show that energy-based models (EBMs) are a promising class of models to use for model-based planning. EBMs naturally support inference of intermediate states given start and goal state dis... | [] | null | null | 1909.06878 | title_snapshot |
v100/wong20a | Identifying Unknown Instances for Autonomous Driving | https://proceedings.mlr.press/v100/wong20a.html | [
"Kelvin Wong",
"Shenlong Wang",
"Mengye Ren",
"Ming Liang",
"Raquel Urtasun"
] | null | null | In the past few years, we have seen great progress in perception algorithms, particular through the use of deep learning. However, most existing approaches focus on a few categories of interest, which represent only a small fraction of the potential categories that robots need to handle in the real-world. Thus, identif... | [] | null | null | 1910.11296 | title_snapshot |
v100/thomason20a | Vision-and-Dialog Navigation | https://proceedings.mlr.press/v100/thomason20a.html | [
"Jesse Thomason",
"Michael Murray",
"Maya Cakmak",
"Luke Zettlemoyer"
] | null | null | Robots navigating in human environments should use language to ask for assistance and be able to understand human responses. To study this challenge, we introduce Cooperative Vision-and-Dialog Navigation, a dataset of over 2k embodied, human-human dialogs situated in simulated, photorealistic home environments. The Nav... | [] | null | null | 1907.04957 | title_snapshot |
v100/jain20a | Discrete Residual Flow for Probabilistic Pedestrian Behavior Prediction | https://proceedings.mlr.press/v100/jain20a.html | [
"Ajay Jain",
"Sergio Casas",
"Renjie Liao",
"Yuwen Xiong",
"Song Feng",
"Sean Segal",
"Raquel Urtasun"
] | null | null | Self-driving vehicles plan around both static and dynamic objects, applying predictive models of behavior to estimate future locations of the objects in the environment. However, future behavior is inherently uncertain, and models of motion that produce deterministic outputs are limited to short timescales. Particularl... | [] | null | null | 1910.08041 | title_snapshot |
v100/bansal20a | Combining Optimal Control and Learning for Visual Navigation in Novel Environments | https://proceedings.mlr.press/v100/bansal20a.html | [
"Somil Bansal",
"Varun Tolani",
"Saurabh Gupta",
"Jitendra Malik",
"Claire Tomlin"
] | null | null | Model-based control is a popular paradigm for robot navigation because it can leverage a known dynamics model to efficiently plan robust robot trajectories. However, it is challenging to use model-based methods in settings where the environment is a priori unknown and can only be observed partially through onboard sens... | [] | null | null | 1903.02531 | title_snapshot |
v100/mazoure20a | Leveraging exploration in off-policy algorithms via normalizing flows | https://proceedings.mlr.press/v100/mazoure20a.html | [
"Bogdan Mazoure",
"Thang Doan",
"Audrey Durand",
"Joelle Pineau",
"R Devon Hjelm"
] | null | null | The ability to discover approximately optimal policies in domains with sparse rewards is crucial to applying reinforcement learning (RL) in many real-world scenarios. Approaches such as neural density models and continuous exploration (e.g., Go-Explore) have been proposed to maintain the high exploration rate necessary... | [] | null | null | 1905.06893 | title_snapshot |
v100/allevato20a | TuneNet: One-Shot Residual Tuning for System Identification and Sim-to-Real Robot Task Transfer | https://proceedings.mlr.press/v100/allevato20a.html | [
"Adam Allevato",
"Elaine Schaertl Short",
"Mitch Pryor",
"Andrea Thomaz"
] | null | null | As researchers teach robots to perform more and more complex tasks, the need for realistic simulation environments is growing. Existing techniques for closing the reality gap by approximating real-world physics often require extensive real world data and/or thousands of simulation samples. This paper presents TuneNet, ... | [] | null | null | 1907.11200 | title_snapshot |
v100/antonova20a | Bayesian Optimization in Variational Latent Spaces with Dynamic Compression | https://proceedings.mlr.press/v100/antonova20a.html | [
"Rika Antonova",
"Akshara Rai",
"Tianyu Li",
"Danica Kragic"
] | null | null | Data-efficiency is crucial for autonomous robots to adapt to new tasks and environments. In this work, we focus on robotics problems with a budget of only 10-20 trials. This is a very challenging setting even for data- efficient approaches like Bayesian optimization (BO), especially when optimizing higher-dimensional c... | [] | null | null | 1907.04796 | title_snapshot |
v100/lynnerup20a | A Survey on Reproducibility by Evaluating Deep Reinforcement Learning Algorithms on Real-World Robots | https://proceedings.mlr.press/v100/lynnerup20a.html | [
"Nicolai A. Lynnerup",
"Laura Nolling",
"Rasmus Hasle",
"John Hallam"
] | null | null | As reinforcement learning (RL) achieves more success in solving complex tasks, more care is needed to ensure that RL research is reproducible and that algorithms therein can be compared easily and fairly with minimal bias. RL results are, however, notoriously hard to reproduce due to the algorithms’ intrinsic variance,... | [] | null | null | 1909.03772 | title_snapshot |
v100/wilson20a | Learning to Manipulate Object Collections Using Grounded State Representations | https://proceedings.mlr.press/v100/wilson20a.html | [
"Matthew Wilson",
"Tucker Hermans"
] | null | null | We propose a method for sim-to-real robot learning which exploits simulator state information in a way that scales to many objects. First, we train a pair of encoders on raw object pose targets to learn representations that accurately capture the state information of a multi-object environment. Second, we use these enc... | [] | null | null | 1909.07876 | title_snapshot |
v100/guizilini20a | Robust Semi-Supervised Monocular Depth Estimation with Reprojected Distances | https://proceedings.mlr.press/v100/guizilini20a.html | [
"Vitor Guizilini",
"Jie Li",
"Rares Ambrus",
"Sudeep Pillai",
"Adrien Gaidon"
] | null | null | Dense depth estimation from a single image is a key problem in computer vision, with exciting applications in a multitude of robotic tasks. Initially viewed as a direct regression problem, requiring annotated labels as supervision at training time, in the past few years a substantial amount of work has been done in sel... | [] | null | null | 1910.01765 | title_snapshot |
v100/klink20a | Self-Paced Contextual Reinforcement Learning | https://proceedings.mlr.press/v100/klink20a.html | [
"Pascal Klink",
"Hany Abdulsamad",
"Boris Belousov",
"Jan Peters"
] | null | null | Generalization and adaptation of learned skills to novel situations is a core requirement for intelligent autonomous robots. Although contextual reinforcement learning provides a principled framework for learning and generalization of behaviors across related tasks, it generally relies on uninformed sampling of environ... | [] | null | null | 1910.02826 | title_snapshot |
v100/nair20a | Contextual Imagined Goals for Self-Supervised Robotic Learning | https://proceedings.mlr.press/v100/nair20a.html | [
"Ashvin Nair",
"Shikhar Bahl",
"Alexander Khazatsky",
"Vitchyr Pong",
"Glen Berseth",
"Sergey Levine"
] | null | null | While reinforcement learning provides an appealing formalism for learning individual skills, a general-purpose robotic system must be able to master an extensive repertoire of behaviors. Instead of learning a large collection of skills individually, can we instead enable a robot to propose and practice its own behavior... | [] | null | null | 1910.11670 | title_snapshot |
v100/roh20a | Conditional Driving from Natural Language Instructions | https://proceedings.mlr.press/v100/roh20a.html | [
"Junha Roh",
"Chris Paxton",
"Andrzej Pronobis",
"Ali Farhadi",
"Dieter Fox"
] | null | null | Widespread adoption of self-driving cars will depend not only on their safety but largely on their ability to interact with human users. Just like human drivers, self-driving cars will be expected to understand and safely follow natural-language directions that suddenly alter the pre-planned route according to user’s p... | [] | null | null | 1910.07615 | title_snapshot |
v100/hong20a | Adversarial Active Exploration for Inverse Dynamics Model Learning | https://proceedings.mlr.press/v100/hong20a.html | [
"Zhang-Wei Hong",
"Tsu-Jui Fu",
"Tzu-Yun Shann",
"Chun-Yi Lee"
] | null | null | We present an adversarial active exploration for inverse dynamics model learning, a simple yet effective learning scheme that incentivizes exploration in an environment without any human intervention. Our framework consists of a deep reinforcement learning (DRL) agent and an inverse dynamics model contesting with each ... | [] | null | null | 1806.10019 | title_snapshot |
v100/byravan20a | Imagined Value Gradients: Model-Based Policy Optimization with Tranferable Latent Dynamics Models | https://proceedings.mlr.press/v100/byravan20a.html | [
"Arunkumar Byravan",
"Jost Tobias Springenberg",
"Abbas Abdolmaleki",
"Roland Hafner",
"Michael Neunert",
"Thomas Lampe",
"Noah Siegel",
"Nicolas Heess",
"Martin Riedmiller"
] | null | null | Humans are masters at quickly learning many complex tasks, relying on an approximate understanding of the dynamics of their environments. In much the same way, we would like our learning agents to quickly adapt to new tasks. In this paper, we explore how model-based Reinforcement Learning (RL) can facilitate transfer t... | [] | null | null | 1910.04142 | title_judge |
v100/liu20a | PIC: Permutation Invariant Critic for Multi-Agent Deep Reinforcement Learning | https://proceedings.mlr.press/v100/liu20a.html | [
"Iou-Jen Liu",
"Raymond A. Yeh",
"Alexander G. Schwing"
] | null | null | Sample efficiency and scalability to a large number of agents are two important goals for multi-agent reinforcement learning systems. Recent works got us closer to those goals, addressing non-stationarity of the environment from a single agent’s perspective by utilizing a deep net critic which depends on all observatio... | [] | null | null | 1911.00025 | title_snapshot |
v100/li20a | HRL4IN: Hierarchical Reinforcement Learning for Interactive Navigation with Mobile Manipulators | https://proceedings.mlr.press/v100/li20a.html | [
"Chengshu Li",
"Fei Xia",
"Roberto Martín-Martín",
"Silvio Savarese"
] | null | null | Most common navigation tasks in human environments require auxiliary arm interactions, e.g. opening doors, pressing buttons and pushing obstacles away. This type of navigation tasks, which we call Interactive Navigation, requires the use of mobile manipulators: mobile bases with manipulation capabilities. Interactive N... | [] | null | null | 1910.11432 | title_snapshot |
v100/kumar20a | Learning Navigation Subroutines from Egocentric Videos | https://proceedings.mlr.press/v100/kumar20a.html | [
"Ashish Kumar",
"Saurabh Gupta",
"Jitendra Malik"
] | null | null | Planning at a higher level of abstraction instead of low level torques improves the sample efficiency in reinforcement learning, and computational efficiency in classical planning. We propose a method to learn such hierarchical abstractions, or subroutines from egocentric video data of experts performing tasks. We lear... | [] | null | null | 1905.12612 | title_snapshot |
v100/heim20a | A Learnable Safety Measure | https://proceedings.mlr.press/v100/heim20a.html | [
"Steve Heim",
"Alexander von Rohr",
"Sebastian Trimpe",
"Alexander Badri-Spröwitz"
] | null | null | Failures are challenging for learning to control physical systems since they risk damage, time-consuming resets, and often provide little gradient information. Adding safety constraints to exploration typically requires a lot of prior knowledge and domain expertise. We present a safety measure which implicitly captures... | [] | null | null | 1910.02835 | title_snapshot |
v100/lutter20a | HJB Optimal Feedback Control with Deep Differential Value Functions and Action Constraints | https://proceedings.mlr.press/v100/lutter20a.html | [
"Michael Lutter",
"Boris Belousov",
"Kim Listmann",
"Debora Clever",
"Jan Peters"
] | null | null | Learning optimal feedback control laws capable of executing optimal trajectories is essential for many robotic applications. Such policies can be learned using reinforcement learning or planned using optimal control. While reinforcement learning is sample inefficient, optimal control only plans an optimal trajectory fr... | [] | null | null | 1909.06153 | title_snapshot |
v100/jung20a | Multi-Frame GAN: Image Enhancement for Stereo Visual Odometry in Low Light | https://proceedings.mlr.press/v100/jung20a.html | [
"Eunah Jung",
"Nan Yang",
"Daniel Cremers"
] | null | null | We propose the concept of a multi-frame GAN (MFGAN) and demonstrate its potential as an image sequence enhancement for stereo visual odometry in low light conditions. We base our method on an invertible adversarial network to transfer the beneficial features of brightly illuminated scenes to the sequence in poor illumi... | [] | null | null | 1910.06632 | title_snapshot |
v100/lin20a | Connectivity Guaranteed Multi-robot Navigation via Deep Reinforcement Learning | https://proceedings.mlr.press/v100/lin20a.html | [
"Juntong Lin",
"Xuyun Yang",
"Peiwei Zheng",
"Hui Cheng"
] | null | null | This paper considers the multi-robot navigation problem where the geometric center of a multi-robot team aims to efficiently reach the waypoint without collisions in unknown complex environments while maintaining connectivity during the navigation. A novel Deep Reinforcement Learning (DRL)-based approach is proposed to... | [] | null | null | null | null |
v100/tolstaya20a | Learning Decentralized Controllers for Robot Swarms with Graph Neural Networks | https://proceedings.mlr.press/v100/tolstaya20a.html | [
"Ekaterina Tolstaya",
"Fernando Gama",
"James Paulos",
"George Pappas",
"Vijay Kumar",
"Alejandro Ribeiro"
] | null | null | We consider the problem of finding distributed controllers for large networks of mobile robots with interacting dynamics and sparsely available communications. Our approach is to learn local controllers that require only local information and communications at test time by imitating the policy of centralized controller... | [] | null | null | 1903.10527 | title_snapshot |
v100/choromanski20a | Provably Robust Blackbox Optimization for Reinforcement Learning | https://proceedings.mlr.press/v100/choromanski20a.html | [
"Krzysztof Choromanski",
"Aldo Pacchiano",
"Jack Parker-Holder",
"Yunhao Tang",
"Deepali Jain",
"Yuxiang Yang",
"Atil Iscen",
"Jasmine Hsu",
"Vikas Sindhwani"
] | null | null | Interest in derivative-free optimization (DFO) and “evolutionary strategies” (ES) has recently surged in the Reinforcement Learning (RL) community, with growing evidence that they can match state of the art methods for policy optimization problems in Robotics. However, it is well known that DFO methods suffer from proh... | [] | null | null | 1903.02993 | title_snapshot |
v100/watson20a | Stochastic Optimal Control as Approximate Input Inference | https://proceedings.mlr.press/v100/watson20a.html | [
"Joe Watson",
"Hany Abdulsamad",
"Jan Peters"
] | null | null | Optimal control of stochastic nonlinear dynamical systems is a major challenge in the domain of robot learning. Given the intractability of the global control problem, state-of-the-art algorithms focus on approximate sequential optimization techniques, that heavily rely on heuristics for regularization in order to achi... | [] | null | null | 1910.03003 | title_snapshot |
v100/kurenkov20a | AC-Teach: A Bayesian Actor-Critic Method for Policy Learning with an Ensemble of Suboptimal Teachers | https://proceedings.mlr.press/v100/kurenkov20a.html | [
"Andrey Kurenkov",
"Ajay Mandlekar",
"Roberto Martin-Martin",
"Silvio Savarese",
"Animesh Garg"
] | null | null | The exploration mechanism used by a Deep Reinforcement Learning (RL) agent plays a key role in determining its sample efficiency. Thus, improving over random exploration is crucial to solve long-horizon tasks with sparse rewards. We propose to leverage an ensemble of partial solutions as teachers that guide the agent’s... | [] | null | null | 1909.04121 | title_snapshot |
v100/neunert20a | Continuous-Discrete Reinforcement Learning for Hybrid Control in Robotics | https://proceedings.mlr.press/v100/neunert20a.html | [
"Michael Neunert",
"Abbas Abdolmaleki",
"Markus Wulfmeier",
"Thomas Lampe",
"Tobias Springenberg",
"Roland Hafner",
"Francesco Romano",
"Jonas Buchli",
"Nicolas Heess",
"Martin Riedmiller"
] | null | null | Many real-world control problems involve both discrete decision variables – such as the choice of control modes, gear switching or digital outputs – as well as continuous decision variables – such as velocity setpoints, control gains or analogue outputs. However, when defining the corresponding optimal control or reinf... | [] | null | null | 2001.00449 | title_snapshot |
v100/losey20a | Learning from My Partner’s Actions: Roles in Decentralized Robot Teams | https://proceedings.mlr.press/v100/losey20a.html | [
"Dylan P. Losey",
"Mengxi Li",
"Jeannette Bohg",
"Dorsa Sadigh"
] | null | null | When teams of robots collaborate to complete a task, communication is often necessary. Like humans, robot teammates should implicitly communicate through their actions: but interpreting our partner’s actions is typically difficult, since a given action may have many different underlying reasons. Here we propose an alte... | [] | null | null | 1910.07613 | title_snapshot |
v100/wei20a | Energy-efficient Path Planning for Ground Robots by and Combining Air and Ground Measurements | https://proceedings.mlr.press/v100/wei20a.html | [
"Minghan Wei",
"Volkan Isler"
] | null | null | As mobile robots find increasing use in outdoor applications, designing energy-efficient robot navigation algorithms is gaining importance. There are two primary approaches to energy efficient navigation: Offline approaches rely on a previously built energy map as input to a path planner. Obtaining energy maps for larg... | [] | null | null | 1907.06337 | title_judge |
v100/krupnik20a | Multi-Agent Reinforcement Learning with Multi-Step Generative Models | https://proceedings.mlr.press/v100/krupnik20a.html | [
"Orr Krupnik",
"Igor Mordatch",
"Aviv Tamar"
] | null | null | We consider model-based reinforcement learning (MBRL) in 2-agent, high-fidelity continuous control problems – an important domain for robots inter-acting with other agents in the same workspace. For non-trivial dynamical systems, MBRL typically suffers from accumulating errors. Several recent studies have addressed thi... | [] | null | null | 1901.10251 | title_snapshot |
v100/sax20a | Learning to Navigate Using Mid-Level Visual Priors | https://proceedings.mlr.press/v100/sax20a.html | [
"Alexander Sax",
"Jeffrey O. Zhang",
"Bradley Emi",
"Amir Zamir",
"Silvio Savarese",
"Leonidas Guibas",
"Jitendra Malik"
] | null | null | How much does having visual priors about the world (e.g. the fact that the world is 3D) assist in learning to perform downstream motor tasks (e.g. navigating a complex environment)? What are the consequences of not utilizing such visual priors in learning? We study these questions by integrating a generic perceptual sk... | [] | null | null | 1912.11121 | title_snapshot |
v100/chitnis20a | Learning Compact Models for Planning with Exogenous Processes | https://proceedings.mlr.press/v100/chitnis20a.html | [
"Rohan Chitnis",
"Tomás Lozano-Pérez"
] | null | null | We address the problem of approximate model minimization for MDPs in which the state is partitioned into endogenous and (much larger) exogenous components. An exogenous state variable is one whose dynamics are independent of the agent’s actions. We formalize the mask-learning problem, in which the agent must choose a s... | [] | null | null | 1909.13870 | title_snapshot |
v100/khan20a | Graph Policy Gradients for Large Scale Robot Control | https://proceedings.mlr.press/v100/khan20a.html | [
"Arbaaz Khan",
"Ekaterina Tolstaya",
"Alejandro Ribeiro",
"Vijay Kumar"
] | null | null | In this paper, the problem of learning policies to control a large number of homogeneous robots is considered. To this end, we propose a new algorithm we call Graph Policy Gradients (GPG) that exploits the underlying graph symmetry among the robots. The curse of dimensionality one encounters when working with a large n... | [] | null | null | 1907.03822 | title_snapshot |
v100/portelas20a | Teacher algorithms for curriculum learning of Deep RL in continuously parameterized environments | https://proceedings.mlr.press/v100/portelas20a.html | [
"Rémy Portelas",
"Cédric Colas",
"Katja Hofmann",
"Pierre-Yves Oudeyer"
] | null | null | We consider the problem of how a teacher algorithm can enable an unknown Deep Reinforcement Learning (DRL) student to become good at a skill over a wide range of diverse environments. To do so, we study how a teacher algorithm can learn to generate a learning curriculum, whereby it sequentially samples parameters contr... | [] | null | null | 1910.07224 | title_snapshot |
v100/luck20a | Data-efficient Co-Adaptation of Morphology and Behaviour with Deep Reinforcement Learning | https://proceedings.mlr.press/v100/luck20a.html | [
"Kevin Sebastian Luck",
"Heni Ben Amor",
"Roberto Calandra"
] | null | null | Humans and animals are capable of quickly learning new behaviours to solve new tasks. Yet, we often forget that they also rely on a highly specialized morphology that co-adapted with motor control throughout thousands of years. Although compelling, the idea of co-adapting morphology and behaviours in robots is often un... | [] | null | null | 1911.06832 | title_snapshot |
v100/hristov20a | Disentangled Relational Representations for Explaining and Learning from Demonstration | https://proceedings.mlr.press/v100/hristov20a.html | [
"Yordan Hristov",
"Daniel Angelov",
"Michael Burke",
"Alex Lascarides",
"Subramanian Ramamoorthy"
] | null | null | Learning from demonstration is an effective method for human users to instruct desired robot behaviour. However, for most non-trivial tasks of practical interest, efficient learning from demonstration depends crucially on inductive bias in the chosen structure for rewards/costs and policies. We address the case where t... | [] | null | null | 1907.13627 | title_snapshot |
v100/dasari20a | RoboNet: Large-Scale Multi-Robot Learning | https://proceedings.mlr.press/v100/dasari20a.html | [
"Sudeep Dasari",
"Frederik Ebert",
"Stephen Tian",
"Suraj Nair",
"Bernadette Bucher",
"Karl Schmeckpeper",
"Siddharth Singh",
"Sergey Levine",
"Chelsea Finn"
] | null | null | Robot learning has emerged as a promising tool for taming the complexity and diversity of the real world. Methods based on high-capacity models, such as deep networks, hold the promise of providing effective generalization to a wide range of open-world environments. However, these same methods typically require large a... | [] | null | null | 1910.11215 | title_snapshot |
v100/chen20b | Counter-example Guided Learning of Bounds on Environment Behavior | https://proceedings.mlr.press/v100/chen20b.html | [
"Yuxiao Chen",
"Sumanth Dathathri",
"Tung Phan-Minh",
"Richard M. Murray"
] | null | null | There is a growing interest in building autonomous systems that interact with complex environments. The difficulty associated with obtaining an accurate model for such environments poses a challenge to the task of assessing and guaranteeing the system’s performance. We present a data-driven solution that allows for a s... | [] | null | null | 2001.07233 | title_snapshot |
v100/gurumurthy20a | MAME : Model-Agnostic Meta-Exploration | https://proceedings.mlr.press/v100/gurumurthy20a.html | [
"Swaminathan Gurumurthy",
"Sumit Kumar",
"Katia Sycara"
] | null | null | Meta-Reinforcement learning approaches aim to develop learning procedures that can adapt quickly to a distribution of tasks with the help of a few examples. Developing efficient exploration strategies capable of finding the most useful samples becomes critical in such settings. Existing approaches towards finding effic... | [] | null | null | 1911.04024 | title_snapshot |
v100/zhou20a | End-to-End Multi-View Fusion for 3D Object Detection in LiDAR Point Clouds | https://proceedings.mlr.press/v100/zhou20a.html | [
"Yin Zhou",
"Pei Sun",
"Yu Zhang",
"Dragomir Anguelov",
"Jiyang Gao",
"Tom Ouyang",
"James Guo",
"Jiquan Ngiam",
"Vijay Vasudevan"
] | null | null | Recent work on 3D object detection advocates point cloud voxelization in birds-eye view, where objects preserve their physical dimensions and are naturally separable. When represented in this view, however, point clouds are sparse and have highly variable point density, which may cause detectors difficulties in detecti... | [] | null | null | 1910.06528 | title_snapshot |
v100/noseworthy20a | Task-Conditioned Variational Autoencoders for Learning Movement Primitives | https://proceedings.mlr.press/v100/noseworthy20a.html | [
"Michael Noseworthy",
"Rohan Paul",
"Subhro Roy",
"Daehyung Park",
"Nicholas Roy"
] | null | null | Consider a task such as pouring liquid from a cup into a container. Some parameters, such as the location of the pour, are crucial to task success, while others, such as the length of the pour, can exhibit larger variation. In this work, we propose a method that differentiates between specified task parameters and lear... | [] | null | null | null | null |
v100/jha20a | Quasi-Newton Trust Region Policy Optimization | https://proceedings.mlr.press/v100/jha20a.html | [
"Devesh K. Jha",
"Arvind U. Raghunathan",
"Diego Romeres"
] | null | null | We propose a trust region method for policy optimization that employs Quasi-Newton approximation for the Hessian, called Quasi-Newton Trust Region Policy Optimization (QNTRPO). Gradient descent is the de facto algorithm for reinforcement learning tasks with continuous controls. The algorithm has achieved state-of-the-a... | [] | null | null | 1912.11912 | title_snapshot |
v100/kim20a | Learning value functions with relational state representations for guiding task-and-motion planning | https://proceedings.mlr.press/v100/kim20a.html | [
"Beomjoon Kim",
"Luke Shimanuki"
] | null | null | We propose a novel relational state representation and an action-value function learning algorithm that learns from planning experience for geometric task-and-motion planning (GTAMP) problems, in which the goal is to move several objects to regions in the presence of movable obstacles. The representation encodes inform... | [] | null | null | null | null |
v100/williams20a | Locally Weighted Regression Pseudo-Rehearsal for Adaptive Model Predictive Control | https://proceedings.mlr.press/v100/williams20a.html | [
"Grady R. Williams",
"Brian Goldfain",
"Keuntaek Lee",
"Jason Gibson",
"James M. Rehg",
"Evangelos A. Theodorou"
] | null | null | We consider the problem of online adaptation of a neural network designed to represent system dynamics. The neural network model is intended to be used by an MPC control law for autonomous control. This problem is challenging because both input and target distributions are non-stationary, and naive approaches to online... | [] | null | null | 1905.05162 | title_judge |
v100/sieb20a | Graph-Structured Visual Imitation | https://proceedings.mlr.press/v100/sieb20a.html | [
"Maximilian Sieb",
"Zhou Xian",
"Audrey Huang",
"Oliver Kroemer",
"Katerina Fragkiadaki"
] | null | null | We cast visual imitation as a visual correspondence problem. Our robotic agent is rewarded when its actions result in better matching of relative spatial configurations for corresponding visual entities detected in its workspace and the teacher’s demonstration. We build upon recent advances in Computer Vision, such as ... | [] | null | null | 1907.05518 | title_snapshot |
v100/hoeller20a | Deep Value Model Predictive Control | https://proceedings.mlr.press/v100/hoeller20a.html | [
"David Hoeller",
"Farbod Farshidian",
"Marco Hutter"
] | null | null | In this paper, we introduce an actor-critic algorithm called Deep Value Model Predictive Control (DMPC), which combines model-based trajectory optimization with value function estimation. The DMPC actor is a Model Predictive Control (MPC) optimizer with an objective function defined in terms of a value function estimat... | [] | null | null | 1910.03358 | title_snapshot |
v100/park20a | Inferring Task Goals and Constraints using Bayesian Nonparametric Inverse Reinforcement Learning | https://proceedings.mlr.press/v100/park20a.html | [
"Daehyung Park",
"Michael Noseworthy",
"Rohan Paul",
"Subhro Roy",
"Nicholas Roy"
] | null | null | Recovering an unknown reward function for complex manipulation tasks is the fundamental problem of Inverse Reinforcement Learning (IRL). Often, the recovered reward function fails to explicitly capture implicit constraints (e.g., axis alignment, force, or relative alignment) between the manipulator, the objects of inte... | [] | null | null | null | null |
v100/yen-chen20a | Experience-Embedded Visual Foresight | https://proceedings.mlr.press/v100/yen-chen20a.html | [
"Lin Yen-Chen",
"Maria Bauza",
"Phillip Isola"
] | null | null | Visual foresight gives an agent a window into the future, which it can use to anticipate events before they happen and plan strategic behavior. Although impressive results have been achieved on video prediction in constrained settings, these models fail to generalize when confronted with unfamiliar real-world objects. ... | [] | null | null | 1911.05071 | title_snapshot |
v100/gupta20a | Relay Policy Learning: Solving Long-Horizon Tasks via Imitation and Reinforcement Learning | https://proceedings.mlr.press/v100/gupta20a.html | [
"Abhishek Gupta",
"Vikash Kumar",
"Corey Lynch",
"Sergey Levine",
"Karol Hausman"
] | null | null | We present relay policy learning, a method for imitation and reinforcement learning that can solve multi-stage, long-horizon robotic tasks. This general and universally-applicable, two-phase approach consists of an imitation learning stage resulting in goal-conditioned hierarchical policies that can be easily improved ... | [] | null | null | 1910.11956 | title_snapshot |
v100/huang20a | Nonverbal Robot Feedback for Human Teachers | https://proceedings.mlr.press/v100/huang20a.html | [
"Sandy H. Huang",
"Isabella Huang",
"Ravi Pandya",
"Anca D. Dragan"
] | null | null | Robots can learn preferences from human demonstrations, but their success depends on how informative these demonstrations are. Being informative is unfortunately very challenging, because during teaching, people typically get no transparency into what the robot already knows or has learned so far. In contrast, human st... | [] | null | null | 1911.02320 | title_snapshot |
v100/ambrus20a | Two Stream Networks for Self-Supervised Ego-Motion Estimation | https://proceedings.mlr.press/v100/ambrus20a.html | [
"Rares Ambrus",
"Vitor Guizilini",
"Jie Li",
"Sudeep Pillai Adrien Gaidon"
] | null | null | Learning depth and camera ego-motion from raw unlabeled RGB video streams is seeing exciting progress through self-supervision from strong geometric cues. To leverage not only appearance but also scene geometry, we propose a novel self-supervised two-stream network using RGB and inferred depth information for accurate ... | [] | null | null | 1910.01764 | title_snapshot |
v100/wu20b | Model-based Behavioral Cloning with Future Image Similarity Learning | https://proceedings.mlr.press/v100/wu20b.html | [
"Alan Wu",
"AJ Piergiovanni",
"Michael S. Ryoo"
] | null | null | We present a visual imitation learning framework that enables learning of robot action policies solely based on expert samples without any robot trials. Robot exploration and on-policy trials in a real-world environment could often be expensive/dangerous. We present a new approach to address this problem by learning a ... | [] | null | null | 1910.03157 | title_snapshot |
v100/tang20a | Worst Cases Policy Gradients | https://proceedings.mlr.press/v100/tang20a.html | [
"Yichuan Charlie Tang",
"Jian Zhang",
"Ruslan Salakhutdinov"
] | null | null | Recent advances in deep reinforcement learning have demonstrated the capability of learning complex control policies from many types of environment. When learning policies for safety critical applications, it is important to be sensitive to risks and avoid catastrophic events. Towards this goal, we propose an actor-cri... | [] | null | null | 1911.03618 | title_snapshot |
v100/yu20a | Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning | https://proceedings.mlr.press/v100/yu20a.html | [
"Tianhe Yu",
"Deirdre Quillen",
"Zhanpeng He",
"Ryan Julian",
"Karol Hausman",
"Chelsea Finn",
"Sergey Levine"
] | null | null | Meta-reinforcement learning algorithms can enable robots to acquire new skills much more quickly, by leveraging prior experience to learn how to learn. However, much of the current research on meta-reinforcement learning focuses on task distributions that are very narrow. For example, a commonly used meta-reinforcement... | [] | null | null | 1910.10897 | title_snapshot |
v100/nagabandi20a | Deep Dynamics Models for Learning Dexterous Manipulation | https://proceedings.mlr.press/v100/nagabandi20a.html | [
"Anusha Nagabandi",
"Kurt Konolige",
"Sergey Levine",
"Vikash Kumar"
] | null | null | Dexterous multi-fingered hands can provide robots with the ability to flexibly perform a wide range of manipulation skills. However, many of the more complex behaviors are also notoriously difficult to control: Performing in-hand object manipulation, executing finger gaits to move objects, and exhibiting precise fine m... | [] | null | null | 1909.11652 | title_snapshot |
v100/lynch20a | Learning Latent Plans from Play | https://proceedings.mlr.press/v100/lynch20a.html | [
"Corey Lynch",
"Mohi Khansari",
"Ted Xiao",
"Vikash Kumar",
"Jonathan Tompson",
"Sergey Levine",
"Pierre Sermanet"
] | null | null | Acquiring a diverse repertoire of general-purpose skills remains an open challenge for robotics. In this work, we propose self-supervising control on top of human teleoperated play data as a way to scale up skill learning. Play has two properties that make it attractive compared to conventional task demonstrations. Pla... | [] | null | null | 1903.01973 | title_snapshot |
v100/mitash20a | Scene-level Pose Estimation for Multiple Instances of Densely Packed Objects | https://proceedings.mlr.press/v100/mitash20a.html | [
"Chaitanya Mitash",
"Bowen Wen",
"Kostas Bekris",
"Abdeslam Boularias"
] | null | null | This paper introduces key machine learning operations that allow the realization of robust, joint 6D pose estimation of multiple instances of objects either densely packed or in unstructured piles from RGB-D data. The first objective is to learn semantic and instance-boundary detectors without manual labeling. An adver... | [] | null | null | 1910.04953 | title_snapshot |
v100/xiao20a | Macro-Action-Based Deep Multi-Agent Reinforcement Learning | https://proceedings.mlr.press/v100/xiao20a.html | [
"Yuchen Xiao",
"Joshua Hoffman",
"Christopher Amato"
] | null | null | In real-world multi-robot systems, performing high-quality, collaborative behaviors requires robots to asynchronously reason about high-level action selection at varying time durations. Macro-Action Decentralized Partially Observable Markov Decision Processes (MacDec-POMDPs) provide a general framework for asynchronous... | [] | null | null | 2004.08646 | title_snapshot |
v100/mehta20a | Active Domain Randomization | https://proceedings.mlr.press/v100/mehta20a.html | [
"Bhairav Mehta",
"Manfred Diaz",
"Florian Golemo",
"Christopher J. Pal",
"Liam Paull"
] | null | null | Domain randomization is a popular technique for improving domain transfer, often used in a zero-shot setting when the target domain is unknown or cannot easily be used for training. In this work, we empirically examine the effects of domain randomization on agent generalization. Our experiments show that domain randomi... | [] | null | null | 1904.04762 | title_snapshot |
v100/b-iy-ik20a | Asking Easy Questions: A User-Friendly Approach to Active Reward Learning | https://proceedings.mlr.press/v100/b-iy-ik20a.html | [
"Erdem B\\iy\\ik",
"Malayandi Palan",
"Nicholas C. Landolfi",
"Dylan P. Losey",
"Dorsa Sadigh"
] | null | null | Robots can learn the right reward function by querying a human expert. Existing approaches attempt to choose questions where the robot is most uncertain about the human’s response; however, they do not consider how easy it will be for the human to answer! In this paper we explore an information gain formulation for opt... | [] | null | null | 1910.04365 | title_snapshot |
v100/luo20a | Dynamic Experience Replay | https://proceedings.mlr.press/v100/luo20a.html | [
"Jieliang Luo",
"Hui Li"
] | null | null | We present a novel technique called Dynamic Experience Replay (DER) that allows Reinforcement Learning (RL) algorithms to use experience replay samples not only from human demonstrations but also successful transitions generated by RL agents during training and therefore improve training efficiency. It can be combined ... | [] | null | null | 2003.02372 | title_snapshot |
v100/patki20a | Language-guided Semantic Mapping and Mobile Manipulation in Partially Observable Environments | https://proceedings.mlr.press/v100/patki20a.html | [
"Siddharth Patki",
"Ethan Fahnestock",
"Thomas M. Howard",
"Matthew R. Walter"
] | null | null | Recent advances in data-driven models for grounded language understanding have enabled robots to interpret increasingly complex instructions. Two fundamental limitations of these methods are that most require a full model of the environment to be known a priori, and they attempt to reason over a world representation th... | [] | null | null | 1910.10034 | title_snapshot |
v100/chou20a | Learning Parametric Constraints in High Dimensions from Demonstrations | https://proceedings.mlr.press/v100/chou20a.html | [
"Glen Chou",
"Necmiye Ozay",
"Dmitry Berenson"
] | null | null | We present a scalable algorithm for learning parametric constraints in high dimensions from safe expert demonstrations. To reduce the ill-posedness of the constraint recovery problem, our method uses hit-and-run sampling to generate lower cost, and thus unsafe, trajectories. Both safe and unsafe trajectories are used t... | [] | null | null | 1910.03477 | title_snapshot |
v100/evans20a | Variational Optimization Based Reinforcement Learning for Infinite Dimensional Stochastic Systems | https://proceedings.mlr.press/v100/evans20a.html | [
"Ethan N. Evans",
"Marcus A. Periera",
"George I. Boutselis",
"Evangelos A. Theodorou"
] | null | null | Systems involving Partial Differential Equations (PDEs) have recently become more popular among the machine learning community. However prior methods usually treat infinite dimensional problems in finite dimensions with Reduced Order Models. This leads to committing to specific approximation schemes and subsequent deri... | [] | null | null | 1907.04006 | title_snapshot |
v100/saran20a | Understanding Teacher Gaze Patterns for Robot Learning | https://proceedings.mlr.press/v100/saran20a.html | [
"Akanksha Saran",
"Elaine Schaertl Short",
"Andrea Thomaz",
"Scott Niekum"
] | null | null | Human gaze is known to be a strong indicator of underlying human intentions and goals during manipulation tasks. This work studies gaze patterns of human teachers demonstrating tasks to robots and proposes ways in which such patterns can be used to enhance robot learning. Using both kinesthetic teaching and video demon... | [] | null | null | 1907.07202 | title_snapshot |
v100/ghasemipour20a | A Divergence Minimization Perspective on Imitation Learning Methods | https://proceedings.mlr.press/v100/ghasemipour20a.html | [
"Seyed Kamyar Seyed Ghasemipour",
"Richard Zemel",
"Shixiang Gu"
] | null | null | In many settings, it is desirable to learn decision-making and control policies through learning or bootstrapping from expert demonstrations. The most common approaches under this Imitation Learning (IL) framework are Behavioural Cloning (BC), and Inverse Reinforcement Learning (IRL). Recent methods for IRL have demons... | [] | null | null | 1911.02256 | title_snapshot |
v100/schultheis20a | Receding Horizon Curiosity | https://proceedings.mlr.press/v100/schultheis20a.html | [
"Matthias Schultheis",
"Boris Belousov",
"Hany Abdulsamad",
"Jan Peters"
] | null | null | Sample-efficient exploration is crucial not only for discovering rewarding experiences but also for adapting to environment changes in a task-agnostic fashion. A principled treatment of the problem of optimal input synthesis for system identification is provided within the framework of sequential Bayesian experimental ... | [] | null | null | 1910.03620 | title_snapshot |
v100/abbatematteo20a | Learning to Generalize Kinematic Models to Novel Objects | https://proceedings.mlr.press/v100/abbatematteo20a.html | [
"Ben Abbatematteo",
"Stefanie Tellex",
"George Konidaris"
] | null | null | Robots operating in human environments must be capable of interacting with a wide variety of articulated objects such as cabinets, refrigerators, and drawers. Existing approaches require human demonstration or minutes of interaction to fit kinematic models to each novel object from scratch. We present a framework for e... | [] | null | null | null | null |
v100/ahn20a | ROBEL: Robotics Benchmarks for Learning with Low-Cost Robots | https://proceedings.mlr.press/v100/ahn20a.html | [
"Michael Ahn",
"Henry Zhu",
"Kristian Hartikainen",
"Hugo Ponte",
"Abhishek Gupta",
"Sergey Levine",
"Vikash Kumar"
] | null | null | ROBEL is an open-source platform of cost-effective robots designed for reinforcement learning in the real world. ROBEL introduces two robots, each aimed to accelerate reinforcement learning research in different task domains: D’Claw is a three-fingered hand robot that facilitates learning dexterous manipulation tasks, ... | [] | null | null | 1909.11639 | title_snapshot |
v100/weiss20a | Navigation Agents for the Visually Impaired: A Sidewalk Simulator and Experiments | https://proceedings.mlr.press/v100/weiss20a.html | [
"Martin Weiss",
"Simon Chamorro",
"Roger Girgis",
"Margaux Luck",
"Samira E. Kahou",
"Joseph P. Cohen",
"Derek Nowrouzezahrai",
"Doina Precup",
"Florian Golemo",
"Chris Pal"
] | null | null | Millions of blind and visually-impaired (BVI) people navigate urban environments everyday, using smartphones for high-level path-planning and white canes or guide dogs for local information. However, many BVI people still struggle to travel to new places. In our endeavour to create a navigation assistant for the BVI, w... | [] | null | null | 1910.13249 | title_snapshot |
v100/lutjens20a | Certified Adversarial Robustness for Deep Reinforcement Learning | https://proceedings.mlr.press/v100/lutjens20a.html | [
"Björn Lütjens",
"Michael Everett",
"Jonathan P. How"
] | null | null | Deep Neural Network-based systems are now the state-of-the-art in many robotics tasks, but their application in safety-critical domains remains dangerous without formal guarantees on network robustness. Small perturbations to sensor inputs (from noise or adversarial examples) are often enough to change network-based de... | [] | null | null | 1910.12908 | title_snapshot |
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