CoRL
Collection
Accepted papers for CoRL (Conference on Robot Learning), one dataset per year. • 10 items • Updated
paper_id stringlengths 9 23 | title stringlengths 21 132 | paper_url stringlengths 44 58 | authors listlengths 2 12 | type stringclasses 0
values | primary_area stringclasses 0
values | abstract large_stringlengths 591 2.09k | keywords listlengths 0 0 | TL;DR large_stringclasses 0
values | submission_number int64 | arxiv_id stringlengths 10 10 ⌀ | arxiv_id_source stringclasses 3
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|---|---|---|---|---|---|---|---|---|---|---|---|
v87/mueller18a | Driving Policy Transfer via Modularity and Abstraction | https://proceedings.mlr.press/v87/mueller18a.html | [
"Matthias Mueller",
"Alexey Dosovitskiy",
"Bernard Ghanem",
"Vladlen Koltun"
] | null | null | End-to-end approaches to autonomous driving have high sample complexity and are difficult to scale to realistic urban driving. Simulation can help end-to-end driving systems by providing a cheap, safe, and diverse training environment. Yet training driving policies in simulation brings up the problem of transferring su... | [] | null | null | 1804.09364 | title_snapshot |
v87/ohnbar18a | Personalized Dynamics Models for Adaptive Assistive Navigation Systems | https://proceedings.mlr.press/v87/ohnbar18a.html | [
"Eshed OhnBar",
"Kris Kitani",
"Chieko Asakawa"
] | null | null | Consider an assistive system that guides visually impaired users through speech and haptic feedback to their destination. Existing robotic and ubiquitous navigation technologies (e.g., portable, ground, or wearable systems) often operate in a generic, user-agnostic manner. However, to minimize confusion and navigation ... | [] | null | null | 1804.04118 | title_snapshot |
v87/xie18a | Few-Shot Goal Inference for Visuomotor Learning and Planning | https://proceedings.mlr.press/v87/xie18a.html | [
"Annie Xie",
"Avi Singh",
"Sergey Levine",
"Chelsea Finn"
] | null | null | Reinforcement learning and planning methods require an objective or reward function that encodes the desired behavior. Yet, in practice, there is a wide range of scenarios where an objective is difficult to provide programmatically, such as tasks with visual observations involving unknown object positions or deformable... | [] | null | null | 1810.00482 | title_snapshot |
v87/das18a | Neural Modular Control for Embodied Question Answering | https://proceedings.mlr.press/v87/das18a.html | [
"Abhishek Das",
"Georgia Gkioxari",
"Stefan Lee",
"Devi Parikh",
"Dhruv Batra"
] | null | null | We present a modular approach for learning policies for navigation over long planning horizons from language input. Our hierarchical policy operates at multiple timescales, where the higher-level master policy proposes subgoals to be executed by specialized sub-policies. Our choice of subgoals is compositional and sema... | [] | null | null | 1810.11181 | title_snapshot |
v87/yang18a | Visual Curiosity: Learning to Ask Questions to Learn Visual Recognition | https://proceedings.mlr.press/v87/yang18a.html | [
"Jianwei Yang",
"Jiasen Lu",
"Stefan Lee",
"Dhruv Batra",
"Devi Parikh"
] | null | null | In an open-world setting, it is inevitable that an intelligent agent (e.g., a robot) will encounter visual objects, attributes or relationships it does not recognize. In this work, we develop an agent empowered with visual curiosity, i.e. the ability to ask questions to an Oracle (e.g., human) about the contents in ima... | [] | null | null | 1810.00912 | title_snapshot |
v87/yu18a | Guided Feature Transformation (GFT): A Neural Language Grounding Module for Embodied Agents | https://proceedings.mlr.press/v87/yu18a.html | [
"Haonan Yu",
"Xiaochen Lian",
"Haichao Zhang",
"Wei Xu"
] | null | null | Recently there has been a rising interest in training agents, embodied in virtual environments, to perform language-directed tasks by deep reinforcement learning. In this paper, we propose a simple but effective neural language grounding module for embodied agents that can be trained end to end from scratch taking raw ... | [] | null | null | 1805.08329 | title_snapshot |
v87/jang18a | Grasp2Vec: Learning Object Representations from Self-Supervised Grasping | https://proceedings.mlr.press/v87/jang18a.html | [
"Eric Jang",
"Coline Devin",
"Vincent Vanhoucke",
"Sergey Levine"
] | null | null | Well structured visual representations can make robot learning faster and can improve generalization. In this paper, we study how we can acquire effective object-centric representations for robotic manipulation tasks without human labeling by using autonomous robot interaction with the environment. Such representation ... | [] | null | null | 1811.06964 | title_snapshot |
v87/zhao18a | Energy-Based Hindsight Experience Prioritization | https://proceedings.mlr.press/v87/zhao18a.html | [
"Rui Zhao",
"Volker Tresp"
] | null | null | In Hindsight Experience Replay (HER), a reinforcement learning agent is trained by treating whatever it has achieved as virtual goals. However, in pre- vious work, the experience was replayed at random, without considering which episode might be the most valuable for learning. In this paper, we develop an energy-based ... | [] | null | null | 1810.01363 | title_snapshot |
v87/losey18a | Including Uncertainty when Learning from Human Corrections | https://proceedings.mlr.press/v87/losey18a.html | [
"Dylan P. Losey",
"Marcia K. O’Malley"
] | null | null | It is difficult for humans to efficiently teach robots how to correctly perform a task. One intuitive solution is for the robot to iteratively learn the human’s preferences from corrections, where the human improves the robot’s current behavior at each iteration. When learning from corrections, we argue that while the ... | [] | null | null | 1806.02454 | title_snapshot |
v87/kaufmann18a | Deep Drone Racing: Learning Agile Flight in Dynamic Environments | https://proceedings.mlr.press/v87/kaufmann18a.html | [
"Elia Kaufmann",
"Antonio Loquercio",
"Rene Ranftl",
"Alexey Dosovitskiy",
"Vladlen Koltun",
"Davide Scaramuzza"
] | null | null | Autonomous agile flight brings up fundamental challenges in robotics, such as coping with unreliable state estimation, reacting optimally to dynamically changing environments, and coupling perception and action in real time under severe resource constraints. In this paper, we consider these challenges in the context of... | [] | null | null | 1806.08548 | title_snapshot |
v87/yang18b | HDNET: Exploiting HD Maps for 3D Object Detection | https://proceedings.mlr.press/v87/yang18b.html | [
"Bin Yang",
"Ming Liang",
"Raquel Urtasun"
] | null | null | In this paper we show that High-Definition (HD) maps provide strong priors that can boost the performance and robustness of modern 3D object detectors. Towards this goal, we design a single stage detector that extracts geometric and semantic features from the HD maps. As maps might not be available everywhere, we also ... | [] | null | null | 2012.11704 | title_snapshot |
v87/amiranashvili18a | Motion Perception in Reinforcement Learning with Dynamic Objects | https://proceedings.mlr.press/v87/amiranashvili18a.html | [
"Artemij Amiranashvili",
"Alexey Dosovitskiy",
"Vladlen Koltun",
"Thomas Brox"
] | null | null | In dynamic environments, learned controllers are supposed to take motion into account when selecting the action to be taken. However, in existing reinforcement learning works motion is rarely treated explicitly; it is rather assumed that the controller learns the necessary motion representation from temporal stacks of ... | [] | null | null | 1901.03162 | title_snapshot |
v87/karkus18a | Particle Filter Networks with Application to Visual Localization | https://proceedings.mlr.press/v87/karkus18a.html | [
"Peter Karkus",
"David Hsu",
"Wee Sun Lee"
] | null | null | Particle filtering is a powerful approach to sequential state estimation and finds application in many domains, including robot localization, object tracking, etc. To apply particle filtering in practice, a critical challenge is to construct probabilistic system models, especially for systems with complex dynamics or r... | [] | null | null | 1805.08975 | title_snapshot |
v87/martin18a | Sparse Gaussian Process Temporal Difference Learning for Marine Robot Navigation | https://proceedings.mlr.press/v87/martin18a.html | [
"John Martin",
"Jinkun Wang",
"Brendan Englot"
] | null | null | We present a method for Temporal Difference (TD) learning that addresses several challenges faced by robots learning to navigate in a marine environment. For improved data efficiency, our method reduces TD updates to Gaussian Process regression. To make predictions amenable to online settings, we introduce a sparse app... | [] | null | null | 1810.01217 | title_snapshot |
v87/guizilini18a | Fast 3D Modeling with Approximated Convolutional Kernels | https://proceedings.mlr.press/v87/guizilini18a.html | [
"Vitor Guizilini",
"Fabio Ramos"
] | null | null | This paper introduces a novel regression methodology for 3D reconstruction, with applications in robotics tasks such as terrain modeling and implicit surface calculation. The proposed methodology is based on projections into a high-dimensional space, that is able to fit arbitrarily complex data as a continuous function... | [] | null | null | null | null |
v87/guizilini18b | Unpaired Learning of Dense Visual Depth Estimators for Urban Environments | https://proceedings.mlr.press/v87/guizilini18b.html | [
"Vitor Guizilini",
"Fabio Ramos"
] | null | null | This paper addresses the classical problem of learning-based monocular depth estimation in urban environments, in which a model is trained to directly map a single input image to its corresponding depth values. All currently available techniques treat monocular depth estimation as a regression problem, and thus require... | [] | null | null | null | null |
v87/stein18a | Learning over Subgoals for Efficient Navigation of Structured, Unknown Environments | https://proceedings.mlr.press/v87/stein18a.html | [
"Gregory J. Stein",
"Christopher Bradley",
"Nicholas Roy"
] | null | null | We propose a novel technique for efficiently navigating unknown environments over long horizons by learning to predict properties of unknown space. We generate a dynamic action set defined by the current map, factor the Bellman Equation in terms of these actions, and estimate terms, such as the probability that navigat... | [] | null | null | null | null |
v87/subramani18a | Inferring geometric constraints in human demonstrations | https://proceedings.mlr.press/v87/subramani18a.html | [
"Guru Subramani",
"Michael Zinn",
"Michael Gleicher"
] | null | null | This paper presents an approach for inferring geometric constraints in human demonstrations. In our method, geometric constraint models are built to create representations of kinematic constraints such as fixed point, axial rotation, prismatic motion, planar motion and others across multiple degrees of freedom. Our met... | [] | null | null | 1810.00140 | title_snapshot |
v87/sauer18a | Conditional Affordance Learning for Driving in Urban Environments | https://proceedings.mlr.press/v87/sauer18a.html | [
"Axel Sauer",
"Nikolay Savinov",
"Andreas Geiger"
] | null | null | Most existing approaches to autonomous driving fall into one of two categories: modular pipelines, that build an extensive model of the environment, and imitation learning approaches, that map images directly to control outputs. A recently proposed third paradigm, direct perception, aims to combine the advantages of bo... | [] | null | null | 1806.06498 | title_snapshot |
v87/wenzel18a | Modular Vehicle Control for Transferring Semantic Information Between Weather Conditions Using GANs | https://proceedings.mlr.press/v87/wenzel18a.html | [
"Patrick Wenzel",
"Qadeer Khan",
"Daniel Cremers",
"Laura Leal-Taixe"
] | null | null | Even though end-to-end supervised learning has shown promising results for sensorimotor control of self-driving cars, its performance is greatly affected by the weather conditions under which it was trained, showing poor generalization to unseen conditions. In this paper, we show how knowledge can be transferred using ... | [] | null | null | 1807.01001 | title_snapshot |
v87/liang18a | GPU-Accelerated Robotic Simulation for Distributed Reinforcement Learning | https://proceedings.mlr.press/v87/liang18a.html | [
"Jacky Liang",
"Viktor Makoviychuk",
"Ankur Handa",
"Nuttapong Chentanez",
"Miles Macklin",
"Dieter Fox"
] | null | null | Most Deep Reinforcement Learning (Deep RL) algorithms require a prohibitively large number of training samples for learning complex tasks. Many recent works on speeding up Deep RL have focused on distributed training and simulation. While distributed training is often done on the GPU, simulation is not. In this work, w... | [] | null | null | 1810.05762 | title_snapshot |
v87/ushani18a | Feature Learning for Scene Flow Estimation from LIDAR | https://proceedings.mlr.press/v87/ushani18a.html | [
"Arash K. Ushani",
"Ryan M. Eustice"
] | null | null | To perform tasks in dynamic environments, many mobile robots must estimate the motion in the surrounding world. Recently, techniques have been developed to estimate scene flow directly from LIDAR scans, relying on hand-designed features. In this work, we build an encoding network to learn features from an occupancy gri... | [] | null | null | null | null |
v87/majumdar18a | PAC-Bayes Control: Synthesizing Controllers that Provably Generalize to Novel Environments | https://proceedings.mlr.press/v87/majumdar18a.html | [
"Anirudha Majumdar",
"Maxwell Goldstein"
] | null | null | Our goal is to synthesize controllers for robots that provably generalize well to novel environments given a dataset of example environments. The key technical idea behind our approach is to leverage tools from generalization theory in machine learning by exploiting a precise analogy (which we present in the form of a ... | [] | null | null | 1806.04225 | title_judge |
v87/tremblay18a | Deep Object Pose Estimation for Semantic Robotic Grasping of Household Objects | https://proceedings.mlr.press/v87/tremblay18a.html | [
"Jonathan Tremblay",
"Thang To",
"Balakumar Sundaralingam",
"Yu Xiang",
"Dieter Fox",
"Stan Birchfield"
] | null | null | Using synthetic data for training deep neural networks for robotic manipulation holds the promise of an almost unlimited amount of pre-labeled training data, generated safely out of harm’s way. One of the key challenges of synthetic data, to date, has been to bridge the so-called reality gap, so that networks trained o... | [] | null | null | 1809.10790 | title_snapshot |
v87/schenck18a | SPNets: Differentiable Fluid Dynamics for Deep Neural Networks | https://proceedings.mlr.press/v87/schenck18a.html | [
"Connor Schenck",
"Dieter Fox"
] | null | null | In this paper we introduce Smooth Particle Networks (SPNets), a framework for integrating fluid dynamics with deep networks. SPNets adds two new layers to the neural network toolbox: ConvSP and ConvSDF, which enable computing physical interactions with unordered particle sets. We use these layers in combination with st... | [] | null | null | 1806.06094 | title_snapshot |
v87/bauza18a | A Data-Efficient Approach to Precise and Controlled Pushing | https://proceedings.mlr.press/v87/bauza18a.html | [
"Maria Bauza",
"Francois R. Hogan",
"Alberto Rodriguez"
] | null | null | Decades of research in control theory have shown that simple controllers, when provided with timely feedback, can control complex systems. Pushing is an example of a complex mechanical system that is difficult to model accurately due to unknown system parameters such as coefficients of friction and pressure distributio... | [] | null | null | 1807.09904 | title_snapshot |
v87/bruce18a | Learning Deployable Navigation Policies at Kilometer Scale from a Single Traversal | https://proceedings.mlr.press/v87/bruce18a.html | [
"Jake Bruce",
"Niko Sunderhauf",
"Piotr Mirowski",
"Raia Hadsell",
"Michael Milford"
] | null | null | Model-free reinforcement learning has recently been shown to be effective at learning navigation policies from complex image input. However, these algorithms tend to require large amounts of interaction with the environment, which can be prohibitively costly to obtain on robots in the real world. We present an approach... | [] | null | null | 1807.05211 | title_snapshot |
v87/brown18a | Risk-Aware Active Inverse Reinforcement Learning | https://proceedings.mlr.press/v87/brown18a.html | [
"Daniel S. Brown",
"Yuchen Cui",
"Scott Niekum"
] | null | null | Active learning from demonstration allows a robot to query a human for specific types of input to achieve efficient learning. Existing work has explored a variety of active query strategies; however, to our knowledge, none of these strategies directly minimize the performance risk of the policy the robot is learning. U... | [] | null | null | 1901.02161 | title_snapshot |
v87/florence18a | Dense Object Nets: Learning Dense Visual Object Descriptors By and For Robotic Manipulation | https://proceedings.mlr.press/v87/florence18a.html | [
"Peter R. Florence",
"Lucas Manuelli",
"Russ Tedrake"
] | null | null | What is the right object representation for manipulation? We would like robots to visually perceive scenes and learn an understanding of the objects in them that (i) is task-agnostic and can be used as a building block for a variety of manipulation tasks, (ii) is generally applicable to both rigid and non-rigid objects... | [] | null | null | 1806.08756 | title_snapshot |
v87/morere18a | Bayesian RL for Goal-Only Rewards | https://proceedings.mlr.press/v87/morere18a.html | [
"Philippe Morere",
"Fabio Ramos"
] | null | null | We address the challenging problem of reinforcement learning under goal-only rewards [1], where rewards are only non-zero when the goal is achieved. This reward definition alleviates the need for cumbersome reward engineering, making the reward formulation trivial. Classic exploration heuristics such as Boltzmann or ep... | [] | null | null | null | null |
v87/vinitsky18a | Benchmarks for reinforcement learning in mixed-autonomy traffic | https://proceedings.mlr.press/v87/vinitsky18a.html | [
"Eugene Vinitsky",
"Aboudy Kreidieh",
"Luc Le Flem",
"Nishant Kheterpal",
"Kathy Jang",
"Cathy Wu",
"Fangyu Wu",
"Richard Liaw",
"Eric Liang",
"Alexandre M. Bayen"
] | null | null | We release new benchmarks in the use of deep reinforcement learning (RL) to create controllers for mixed-autonomy traffic, where connected and autonomous vehicles (CAVs) interact with human drivers and infrastructure. Benchmarks, such as Mujoco or the Arcade Learning Environment, have spurred new research by enabling r... | [] | null | null | null | null |
v87/wang18a | Intervention Aided Reinforcement Learning for Safe and Practical Policy Optimization in Navigation | https://proceedings.mlr.press/v87/wang18a.html | [
"Fan Wang",
"Bo Zhou",
"Ke Chen",
"Tingxiang Fan",
"Xi Zhang",
"Jiangyong Li",
"Hao Tian",
"Jia Pan"
] | null | null | Combining deep neural networks with reinforcement learning has shown great potential in the next-generation intelligent control. However, there are challenges in terms of safety and cost in practical applications. In this pa- per, we propose the Intervention Aided Reinforcement Learning (IARL) framework, which utilizes... | [] | null | null | 1811.06187 | title_snapshot |
v87/cheng18a | Reinforcement Learning of Active Vision for Manipulating Objects under Occlusions | https://proceedings.mlr.press/v87/cheng18a.html | [
"Ricson Cheng",
"Arpit Agarwal",
"Katerina Fragkiadaki"
] | null | null | We consider artificial agents that learn to jointly control their gripper and camera in order to reinforcement learn manipulation policies in the presence of occlusions from distractor objects. Distractors often occlude the object of interest and cause it to disappear from the field of view. We propose hand/eye control... | [] | null | null | 1811.08067 | title_snapshot |
v87/gehring18a | Adaptable replanning with compressed linear action models for learning from demonstrations | https://proceedings.mlr.press/v87/gehring18a.html | [
"Clement Gehring",
"Leslie Pack Kaelbling",
"Tomas Lozano-Perez"
] | null | null | We propose an adaptable and efficient model-based reinforcement learning approach well suited for continuous domains with sparse samples, a setting often encountered when learning from demonstrations. The flexibility of our method originates from the approximate transition models, estimated from data, and the online re... | [] | null | null | null | null |
v87/senanayake18a | Automorphing Kernels for Nonstationarity in Mapping Unstructured Environments | https://proceedings.mlr.press/v87/senanayake18a.html | [
"Ransalu Senanayake",
"Anthony Tompkins",
"Fabio Ramos"
] | null | null | In order to deploy robots in previously unseen and unstructured environments, the robots should have the capacity to learn on their own and adapt to the changes in the environments. For instance, in mobile robotics, a robot should be able to learn a map of the environment from data itself without the intervention of a ... | [] | null | null | null | null |
v87/sarlin18a | Leveraging Deep Visual Descriptors for Hierarchical Efficient Localization | https://proceedings.mlr.press/v87/sarlin18a.html | [
"Paul-Edouard Sarlin",
"Frederic Debraine",
"Marcin Dymczyk",
"Roland Siegwart",
"Cesar Cadena"
] | null | null | Many robotics applications require precise pose estimates despite operating in large and changing environments. This can be addressed by visual localization, using a pre-computed 3D model of the surroundings. The pose estimation then amounts to finding correspondences between 2D keypoints in a query image and 3D points... | [] | null | null | 1809.01019 | title_snapshot |
v87/richards18a | The Lyapunov Neural Network: Adaptive Stability Certification for Safe Learning of Dynamical Systems | https://proceedings.mlr.press/v87/richards18a.html | [
"Spencer M. Richards",
"Felix Berkenkamp",
"Andreas Krause"
] | null | null | Learning algorithms have shown considerable prowess in simulation by allowing robots to adapt to uncertain environments and improve their performance. However, such algorithms are rarely used in practice on safety-critical systems, since the learned policy typically does not yield any safety guarantees. That is, the re... | [] | null | null | 1808.00924 | title_snapshot |
v87/gualtieri18a | Learning 6-DoF Grasping and Pick-Place Using Attention Focus | https://proceedings.mlr.press/v87/gualtieri18a.html | [
"Marcus Gualtieri",
"Robert Platt"
] | null | null | We address a class of manipulation problems where the robot perceives the scene with a depth sensor and can move its end effector in a space with six degrees of freedom—3D position and orientation. Our approach is to formulate the problem as a Markov decision process (MDP) with abstract yet generally applicable state a... | [] | null | null | 1806.06134 | title_snapshot |
v87/laversanne-finot18a | Curiosity Driven Exploration of Learned Disentangled Goal Spaces | https://proceedings.mlr.press/v87/laversanne-finot18a.html | [
"Adrien Laversanne-Finot",
"Alexandre Pere",
"Pierre-Yves Oudeyer"
] | null | null | Intrinsically motivated goal exploration processes enable agents to explore efficiently complex environments with high-dimensional continuous actions. They have been applied successfully to real world robots to discover repertoires of policies producing a wide diversity of effects. Often these algorithms relied on engi... | [] | null | null | 1807.01521 | title_snapshot |
v87/blukis18a | Mapping Navigation Instructions to Continuous Control Actions with Position-Visitation Prediction | https://proceedings.mlr.press/v87/blukis18a.html | [
"Valts Blukis",
"Dipendra Misra",
"Ross A. Knepper",
"Yoav Artzi"
] | null | null | We propose an approach for mapping natural language instructions and raw observations to continuous control of a quadcopter drone. Our model predicts interpretable position-visitation distributions indicating where the agent should go during execution and where it should stop, and uses the predicted distributions to se... | [] | null | null | 1811.04179 | title_snapshot |
v87/biyik18a | Batch Active Preference-Based Learning of Reward Functions | https://proceedings.mlr.press/v87/biyik18a.html | [
"Erdem Biyik",
"Dorsa Sadigh"
] | null | null | Data generation and labeling are usually an expensive part of learning for robotics. While active learning methods are commonly used to tackle the former problem, preference-based learning is a concept that attempts to solve the latter by querying users with preference questions. In this paper, we will develop a new al... | [] | null | null | 1810.04303 | title_snapshot |
v87/clarke18a | Learning Audio Feedback for Estimating Amount and Flow of Granular Material | https://proceedings.mlr.press/v87/clarke18a.html | [
"Samuel Clarke",
"Travers Rhodes",
"Christopher G. Atkeson",
"Oliver Kroemer"
] | null | null | Granular materials produce audio-frequency mechanical vibrations in air and structures when manipulated. These vibrations correlate with both the nature of the events and the intrinsic properties of the materials producing them. We therefore propose learning to use audio-frequency vibrations from contact events to esti... | [] | null | null | null | null |
v87/long18a | HybridNet: Integrating Model-based and Data-driven Learning to Predict Evolution of Dynamical Systems | https://proceedings.mlr.press/v87/long18a.html | [
"Yun Long",
"Xueyuan She",
"Saibal Mukhopadhyay"
] | null | null | The robotic systems continuously interact with complex dynamical systems in the physical world. Reliable predictions of spatiotemporal evolution of these dynamical systems, with limited knowledge of system dynamics, are crucial for autonomous operation. In this paper, we present HybridNet, a framework that integrates d... | [] | null | null | 1806.07439 | title_snapshot |
v87/mahmood18a | Benchmarking Reinforcement Learning Algorithms on Real-World Robots | https://proceedings.mlr.press/v87/mahmood18a.html | [
"A. Rupam Mahmood",
"Dmytro Korenkevych",
"Gautham Vasan",
"William Ma",
"James Bergstra"
] | null | null | Through many recent successes in simulation, model-free reinforcement learning has emerged as a promising approach to solving continuous control robotic tasks. The research community is now able to reproduce, analyze and build quickly on these results due to open source implementations of learning algorithms and simula... | [] | null | null | 1809.07731 | title_snapshot |
v87/shankar18a | Learning Neural Parsers with Deterministic Differentiable Imitation Learning | https://proceedings.mlr.press/v87/shankar18a.html | [
"Tanmay Shankar",
"Nicholas Rhinehart",
"Katharina Muelling",
"Kris M. Kitani"
] | null | null | We explore the problem of learning to decompose spatial tasks into segments, as exemplified by the problem of a painting robot covering a large object. Inspired by the ability of classical decision tree algorithms to construct structured parti- tions of their input spaces, we formulate the problem of decomposing object... | [] | null | null | 1806.07822 | title_snapshot |
v87/barsan18a | Learning to Localize Using a LiDAR Intensity Map | https://proceedings.mlr.press/v87/barsan18a.html | [
"Ioan Andrei Barsan",
"Shenlong Wang",
"Andrei Pokrovsky",
"Raquel Urtasun"
] | null | null | In this paper we propose a real-time, calibration-agnostic and effective localization system for self-driving cars. Our method learns to embed the online LiDAR sweeps and intensity map into a joint deep embedding space. Localization is then conducted through an efficient convolutional matching between the embeddings. O... | [] | null | null | 2012.10902 | title_snapshot |
v87/clavera18a | Model-Based Reinforcement Learning via Meta-Policy Optimization | https://proceedings.mlr.press/v87/clavera18a.html | [
"Ignasi Clavera",
"Jonas Rothfuss",
"John Schulman",
"Yasuhiro Fujita",
"Tamim Asfour",
"Pieter Abbeel"
] | null | null | Model-based reinforcement learning approaches carry the promise of being data efficient. However, due to challenges in learning dynamics models that sufficiently match the real-world dynamics, they struggle to achieve the same asymptotic performance as model-free methods. We propose Model-Based Meta-Policy-Optimization... | [] | null | null | 1809.05214 | title_snapshot |
v87/maeda18a | Reinforcement Learning of Phase Oscillators for Fast Adaptation to Moving Targets | https://proceedings.mlr.press/v87/maeda18a.html | [
"Guilherme Maeda",
"Okan Koc",
"Jun Morimoto"
] | null | null | Online movement generation in tasks involving real humanoid robots interacting with fast-moving targets is extremely difficult. This paper approaches this problem via imitation and reinforcement learning using phase variables. Imitation learning is used to acquire primitive trajectories of the demonstrator interacting ... | [] | null | null | null | null |
v87/antonova18a | Global Search with Bernoulli Alternation Kernel for Task-oriented Grasping Informed by Simulation | https://proceedings.mlr.press/v87/antonova18a.html | [
"Rika Antonova",
"Mia Kokic",
"Johannes A. Stork",
"Danica Kragic"
] | null | null | We develop an approach that benefits from large simulated datasets and takes full advantage of the limited online data that is most relevant. We propose a variant of Bayesian optimization that alternates between using informed and uninformed kernels. With this Bernoulli Alternation Kernel we ensure that discrepancies b... | [] | null | null | 1810.04438 | title_snapshot |
v87/kalashnikov18a | Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation | https://proceedings.mlr.press/v87/kalashnikov18a.html | [
"Dmitry Kalashnikov",
"Alex Irpan",
"Peter Pastor",
"Julian Ibarz",
"Alexander Herzog",
"Eric Jang",
"Deirdre Quillen",
"Ethan Holly",
"Mrinal Kalakrishnan",
"Vincent Vanhoucke",
"Sergey Levine"
] | null | null | In this paper, we study the problem of learning vision-based dynamic manipulation skills using a scalable reinforcement learning approach. We study this problem in the context of grasping, a longstanding challenge in robotic manipulation. In contrast to static learning behaviors that choose a grasp point and then execu... | [] | null | null | 1806.10293 | manual |
v87/romoff18a | Reward Estimation for Variance Reduction in Deep Reinforcement Learning | https://proceedings.mlr.press/v87/romoff18a.html | [
"Joshua Romoff",
"Peter Henderson",
"Alexandre Piche",
"Vincent Francois-Lavet",
"Joelle Pineau"
] | null | null | Reinforcement Learning (RL) agents require the specification of a reward signal for learning behaviours. However, introduction of corrupt or stochastic rewards can yield high variance in learning. Such corruption may be a direct result of goal misspecification, randomness in the reward signal, or correlation of the rew... | [] | null | null | 1805.03359 | title_snapshot |
v87/muratore18a | Domain Randomization for Simulation-Based Policy Optimization with Transferability Assessment | https://proceedings.mlr.press/v87/muratore18a.html | [
"Fabio Muratore",
"Felix Treede",
"Michael Gienger",
"Jan Peters"
] | null | null | Exploration-based reinforcement learning on real robot systems is generally time-intensive and can lead to catastrophic robot failures. Therefore, simulation-based policy search appears to be an appealing alternative. Unfor- tunately, running policy search on a slightly faulty simulator can easily lead to the maximizat... | [] | null | null | null | null |
v87/nyga18a | Grounding Robot Plans from Natural Language Instructions with Incomplete World Knowledge | https://proceedings.mlr.press/v87/nyga18a.html | [
"Daniel Nyga",
"Subhro Roy",
"Rohan Paul",
"Daehyung Park",
"Mihai Pomarlan",
"Michael Beetz",
"Nicholas Roy"
] | null | null | Our goal is to enable robots to interpret and execute high-level tasks conveyed using natural language instructions. For example, consider tasking a household robot to, “prepare my breakfast”, “clear the boxes on the table” or “make me a fruit milkshake”. Interpreting such underspecified instructions requires environme... | [] | null | null | null | null |
v87/chitnis18a | Learning What Information to Give in Partially Observed Domains | https://proceedings.mlr.press/v87/chitnis18a.html | [
"Rohan Chitnis",
"Leslie Pack Kaelbling",
"Tomas Lozano-Perez"
] | null | null | In many robotic applications, an autonomous agent must act within and explore a partially observed environment that is unobserved by its human team-mate. We consider such a setting in which the agent can, while acting, transmit declarative information to the human that helps them understand aspects of this unseen envir... | [] | null | null | 1805.08263 | title_snapshot |
v87/matas18a | Sim-to-Real Reinforcement Learning for Deformable Object Manipulation | https://proceedings.mlr.press/v87/matas18a.html | [
"Jan Matas",
"Stephen James",
"Andrew J. Davison"
] | null | null | We have seen much recent progress in rigid object manipulation, but interaction with deformable objects has notably lagged behind. Due to the large configuration space of deformable objects, solutions using traditional modelling approaches require significant engineering work. Perhaps then, bypassing the need for expli... | [] | null | null | 1806.07851 | title_snapshot |
v87/kumar18a | Expanding Motor Skills using Relay Networks | https://proceedings.mlr.press/v87/kumar18a.html | [
"Visak CV Kumar",
"Sehoon Ha",
"C.Karen Liu"
] | null | null | While recent advances in deep reinforcement learning have achieved impressive results in learning motor skills, many policies are only capable within a limited set of initial states. We propose an algorithm that sequentially decomposes a complex robotic task into simpler subtasks and trains a local policy for each subt... | [] | null | null | 1709.07932 | title_judge |
v87/jain18a | Efficient Hierarchical Robot Motion Planning Under Uncertainty and Hybrid Dynamics | https://proceedings.mlr.press/v87/jain18a.html | [
"Ajinkya Jain",
"Scott Niekum"
] | null | null | Noisy observations coupled with nonlinear dynamics pose one of the biggestchallengesinrobotmotionplanning. Bydecomposingnonlineardynamics into a discrete set of local dynamics models, hybrid dynamics provide a natural way to model nonlinear dynamics, especially in systems with sudden discontinuities in dynamics due to ... | [] | null | null | 1802.04205 | title_snapshot |
v87/fan18a | SURREAL: Open-Source Reinforcement Learning Framework and Robot Manipulation Benchmark | https://proceedings.mlr.press/v87/fan18a.html | [
"Linxi Fan",
"Yuke Zhu",
"Jiren Zhu",
"Zihua Liu",
"Orien Zeng",
"Anchit Gupta",
"Joan Creus-Costa",
"Silvio Savarese",
"Li Fei-Fei"
] | null | null | Reproducibility has been a significant challenge in deep reinforcement learning and robotics research. Open-source frameworks and standardized benchmarks can serve an integral role in rigorous evaluation and reproducible research. We introduce SURREAL, an open-source scalable framework that supports state-of-the-art d... | [] | null | null | null | null |
v87/james18a | Task-Embedded Control Networks for Few-Shot Imitation Learning | https://proceedings.mlr.press/v87/james18a.html | [
"Stephen James",
"Michael Bloesch",
"Andrew J. Davison"
] | null | null | Much like humans, robots should have the ability to leverage knowledge from previously learned tasks in order to learn new tasks quickly in new and unfamiliar environments. Despite this, most robot learning approaches have focused on learning a single task, from scratch, with a limited notion of generalisation, and no ... | [] | null | null | 1810.03237 | title_snapshot |
v87/bobu18a | Learning under Misspecified Objective Spaces | https://proceedings.mlr.press/v87/bobu18a.html | [
"Andreea Bobu",
"Andrea Bajcsy",
"Jaime F. Fisac",
"Anca D. Dragan"
] | null | null | Learning robot objective functions from human input has become increasingly important, but state-of-the-art techniques assume that the human’s desired objective lies within the robot’s hypothesis space. When this is not true, even methods that keep track of uncertainty over the objective fail because they reason about ... | [] | null | null | 1810.05157 | title_snapshot |
v87/kahn18a | Composable Action-Conditioned Predictors: Flexible Off-Policy Learning for Robot Navigation | https://proceedings.mlr.press/v87/kahn18a.html | [
"Gregory Kahn",
"Adam Villaflor",
"Pieter Abbeel",
"Sergey Levine"
] | null | null | A general-purpose intelligent robot must be able to learn autonomously and be able to accomplish multiple tasks in order to be deployed in the real world. However, standard reinforcement learning approaches learn separate task-specific policies and assume the reward function for each task is known a priori. We propose ... | [] | null | null | 1810.07167 | title_snapshot |
v87/golemo18a | Sim-to-Real Transfer with Neural-Augmented Robot Simulation | https://proceedings.mlr.press/v87/golemo18a.html | [
"Florian Golemo",
"Adrien Ali Taiga",
"Aaron Courville",
"Pierre-Yves Oudeyer"
] | null | null | Despite the recent successes of deep reinforcement learning, teaching complex motor skills to a physical robot remains a hard problem. While learning directly on a real system is usually impractical, doing so in simulation has proven to be fast and safe. Nevertheless, because of the "reality gap," policies trained in s... | [] | null | null | null | null |
v87/shan18a | Bayesian Generalized Kernel Inference for Terrain Traversability Mapping | https://proceedings.mlr.press/v87/shan18a.html | [
"Tixiao Shan",
"Jinkun Wang",
"Brendan Englot",
"Kevin Doherty"
] | null | null | We propose a new approach for traversability mapping with sparse lidar scans collected by ground vehicles, which leverages probabilistic inference to build descriptive terrain maps. Enabled by recent developments in sparse kernels, Bayesian generalized kernel inference is applied sequentially to the related problems of... | [] | null | null | null | null |
v87/kaushik18a | Multi-objective Model-based Policy Search for Data-efficient Learning with Sparse Rewards | https://proceedings.mlr.press/v87/kaushik18a.html | [
"Rituraj Kaushik",
"Konstantinos Chatzilygeroudis",
"Jean-Baptiste Mouret"
] | null | null | The most data-efficient algorithms for reinforcement learning in robotics are model-based policy search algorithms, which alternate between learning a dynamical model of the robot and optimizing a policy to maximize the expected return given the model and its uncertainties. However, the current algorithms lack an effec... | [] | null | null | 1806.09351 | title_snapshot |
v87/alet18a | Modular meta-learning | https://proceedings.mlr.press/v87/alet18a.html | [
"Ferran Alet",
"Tomas Lozano-Perez",
"Leslie P. Kaelbling"
] | null | null | Many prediction problems, such as those that arise in the context of robotics, have a simplifying underlying structure that, if known, could accelerate learning. In this paper, we present a strategy for learning a set of neural network modules that can be combined in different ways. We train different modular structure... | [] | null | null | 1806.10166 | title_snapshot |
v87/stouraitis18a | Dyadic collaborative Manipulation through Hybrid Trajectory Optimization | https://proceedings.mlr.press/v87/stouraitis18a.html | [
"Theodoros Stouraitis",
"Iordanis Chatzinikolaidis",
"Michael Gienger",
"Sethu Vijayakumar"
] | null | null | This work provides a principled formalism to address the joint planning problem in dyadic collaborative manipulation (DcM) scenarios by representing the human’s intentions as task space forces and solving the joint problem holistically via model-based optimization. The proposed method is the first to empower robotic ag... | [] | null | null | null | null |
v87/mandlekar18a | ROBOTURK: A Crowdsourcing Platform for Robotic Skill Learning through Imitation | https://proceedings.mlr.press/v87/mandlekar18a.html | [
"Ajay Mandlekar",
"Yuke Zhu",
"Animesh Garg",
"Jonathan Booher",
"Max Spero",
"Albert Tung",
"Julian Gao",
"John Emmons",
"Anchit Gupta",
"Emre Orbay",
"Silvio Savarese",
"Li Fei-Fei"
] | null | null | Imitation Learning has empowered recent advances in learning robotic manipulation tasks by addressing shortcomings of Reinforcement Learning such as exploration and reward specification. However, research in this area has been limited to modest-sized datasets due to the difficulty of collecting large quantities of task... | [] | null | null | 1811.02790 | title_snapshot |
v87/zhang18a | Integrating kinematics and environment context into deep inverse reinforcement learning for predicting off-road vehicle trajectories | https://proceedings.mlr.press/v87/zhang18a.html | [
"Yanfu Zhang",
"Wenshan Wang",
"Rogerio Bonatti",
"Daniel Maturana",
"Sebastian Scherer"
] | null | null | Predicting the motion of a mobile agent from a third-person perspective is an important component for many robotics applications, such as autonomous navigation and tracking. With accurate motion prediction of other agents, robots can plan for more intelligent behaviors to achieve specified objectives, instead of acting... | [] | null | null | 1810.07225 | title_snapshot |
v87/sharma18a | Multiple Interactions Made Easy (MIME): Large Scale Demonstrations Data for Imitation | https://proceedings.mlr.press/v87/sharma18a.html | [
"Pratyusha Sharma",
"Lekha Mohan",
"Lerrel Pinto",
"Abhinav Gupta"
] | null | null | In recent years, we have seen an emergence of data-driven approaches in robotics. However, most existing efforts and datasets are either in simulation or focus on a single task in isolation such as grasping, pushing or poking. In order to make progress and capture the space of manipulation, we would need to collect a l... | [] | null | null | 1810.07121 | title_snapshot |
v87/iscen18a | Policies Modulating Trajectory Generators | https://proceedings.mlr.press/v87/iscen18a.html | [
"Atil Iscen",
"Ken Caluwaerts",
"Jie Tan",
"Tingnan Zhang",
"Erwin Coumans",
"Vikas Sindhwani",
"Vincent Vanhoucke"
] | null | null | We propose an architecture for learning complex controllable behaviors by having simple Policies Modulate Trajectory Generators (PMTG), a powerful combination that can provide both memory and prior knowledge to the controller. The result is a flexible architecture that is applicable to a class of problems with periodic... | [] | null | null | 1910.02812 | title_snapshot |
v87/figueroa18a | A Physically-Consistent Bayesian Non-Parametric Mixture Model for Dynamical System Learning | https://proceedings.mlr.press/v87/figueroa18a.html | [
"Nadia Figueroa",
"Aude Billard"
] | null | null | We propose a physically-consistent Bayesian non-parametric approach for fitting Gaussian Mixture Models (GMM) to trajectory data. Physical-consistency of the GMM is ensured by imposing a prior on the component assignments biased by a novel similarity metric that leverages locality and directionality. The resulting GMM ... | [] | null | null | null | null |
v87/casas18a | IntentNet: Learning to Predict Intention from Raw Sensor Data | https://proceedings.mlr.press/v87/casas18a.html | [
"Sergio Casas",
"Wenjie Luo",
"Raquel Urtasun"
] | null | null | In order to plan a safe maneuver, self-driving vehicles need to understand the intent of other traffic participants. We define intent as a combination of discrete high level behaviors as well as continuous trajectories describing future motion. In this paper we develop a one-stage detector and forecaster that exploits ... | [] | null | null | 2101.07907 | title_snapshot |
v87/hristov18a | Interpretable Latent Spaces for Learning from Demonstration | https://proceedings.mlr.press/v87/hristov18a.html | [
"Yordan Hristov",
"Alex Lascarides",
"Subramanian Ramamoorthy"
] | null | null | Effective human-robot interaction, such as in robot learning from human demonstration, requires the learning agent to be able to ground abstract concepts (such as those contained within instructions) in a corresponding high-dimensional sensory input stream from the world. Models such as deep neural networks, with high ... | [] | null | null | 1807.06583 | title_snapshot |
v87/rebecq18a | ESIM: an Open Event Camera Simulator | https://proceedings.mlr.press/v87/rebecq18a.html | [
"Henri Rebecq",
"Daniel Gehrig",
"Davide Scaramuzza"
] | null | null | Event cameras are revolutionary sensors that work radically differently from standard cameras. Instead of capturing intensity images at a fixed rate, event cameras measure changes of intensity asynchronously, in the form of a stream of events, which encode per-pixel brightness changes. In the last few years, their outs... | [] | null | null | null | null |
v87/ebert18a | Robustness via Retrying: Closed-Loop Robotic Manipulation with Self-Supervised Learning | https://proceedings.mlr.press/v87/ebert18a.html | [
"Frederik Ebert",
"Sudeep Dasari",
"Alex X. Lee",
"Sergey Levine",
"Chelsea Finn"
] | null | null | Prediction is an appealing objective for self-supervised learning of behavioral skills, particularly for autonomous robots. However, effectively utilizing predictive models for control, especially with raw image inputs, poses a number of major challenges. How should the predictions be used? What happens when they are i... | [] | null | null | 1810.03043 | title_snapshot |
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