CoRL
Collection
Accepted papers for CoRL (Conference on Robot Learning), one dataset per year. • 10 items • Updated
paper_id stringlengths 9 21 | title stringlengths 26 112 | paper_url stringlengths 44 56 | authors listlengths 2 7 | type stringclasses 0
values | primary_area stringclasses 0
values | abstract large_stringlengths 485 1.72k | keywords listlengths 0 0 | TL;DR large_stringclasses 0
values | submission_number int64 | arxiv_id stringlengths 10 10 ⌀ | arxiv_id_source stringclasses 1
value |
|---|---|---|---|---|---|---|---|---|---|---|---|
v78/dosovitskiy17a | CARLA: An Open Urban Driving Simulator | https://proceedings.mlr.press/v78/dosovitskiy17a.html | [
"Alexey Dosovitskiy",
"German Ros",
"Felipe Codevilla",
"Antonio Lopez",
"Vladlen Koltun"
] | null | null | We introduce CARLA, an open-source simulator for autonomous driving research. CARLA has been developed from the ground up to support development, training, and validation of autonomous urban driving systems. In addition to open-source code and protocols, CARLA provides open digital assets (urban layouts, buildings, veh... | [] | null | null | 1711.03938 | title_snapshot |
v78/lomonaco17a | CORe50: a New Dataset and Benchmark for Continuous Object Recognition | https://proceedings.mlr.press/v78/lomonaco17a.html | [
"Vincenzo Lomonaco",
"Davide Maltoni"
] | null | null | Continuous/Lifelong learning of high-dimensional data streams is a challenging research problem. In fact, fully retraining models each time new data become available is infeasible, due to computational and storage issues, while naïve incremental strategies have been shown to suffer from catastrophic forgetting. In the ... | [] | null | null | 1705.03550 | title_snapshot |
v78/zuo17a | Fast Residual Forests: Rapid Ensemble Learning for Semantic Segmentation | https://proceedings.mlr.press/v78/zuo17a.html | [
"Yan Zuo",
"Tom Drummond"
] | null | null | In recent times, Convolutional Neural Network (CNN) based approaches have performed exceptionally well in many computer vision related tasks, including classification and segmentation. These approaches have shown that given enough training data and time, they can often perform at a level significantly higher than the a... | [] | null | null | null | null |
v78/maeda17a | Active Incremental Learning of Robot Movement Primitives | https://proceedings.mlr.press/v78/maeda17a.html | [
"Guilherme Maeda",
"Marco Ewerton",
"Takayuki Osa",
"Baptiste Busch",
"Jan Peters"
] | null | null | Robots that can learn over time by interacting with non-technical users must be capable of acquiring new motor skills, incrementally. The problem then is deciding when to teach the robot a new skill or when to rely on the robot generalizing its actions. This decision can be made by the robot if it is provided with mean... | [] | null | null | null | null |
v78/antonova17a | Deep Kernels for Optimizing Locomotion Controllers | https://proceedings.mlr.press/v78/antonova17a.html | [
"Rika Antonova",
"Akshara Rai",
"Christopher G. Atkeson"
] | null | null | Sample efficiency is important when optimizing parameters of locomotion controllers, since hardware experiments are time consuming and expensive. Bayesian Optimization, a sample-efficient optimization framework, has recently been widely applied to address this problem, but further improvements in sample efficiency are ... | [] | null | null | 1707.09062 | title_snapshot |
v78/hu17a | Efficient Automatic Perception System Parameter Tuning On Site without Expert Supervision | https://proceedings.mlr.press/v78/hu17a.html | [
"Humphrey Hu",
"George Kantor"
] | null | null | Many modern perception systems require human engineers to tune parameters in order to adapt to various environments and applications. This incurs a large startup cost when deploying a robotic system by relying on human expertise and ground truth instrumentation. To alleviate this, we propose a technique using empirical... | [] | null | null | null | null |
v78/thomason17a | Opportunistic Active Learning for Grounding Natural Language Descriptions | https://proceedings.mlr.press/v78/thomason17a.html | [
"Jesse Thomason",
"Aishwarya Padmakumar",
"Jivko Sinapov",
"Justin Hart",
"Peter Stone",
"Raymond J. Mooney"
] | null | null | Active learning identifies data points from a pool of unlabeled examples whose labels, if made available, are most likely to improve the predictions of a supervised model. Most research on active learning assumes that an agent has access to the entire pool of unlabeled data and can ask for labels of any data points dur... | [] | null | null | null | null |
v78/lopez-guevara17a | Adaptable Pouring: Teaching Robots Not to Spill using Fast but Approximate Fluid Simulation | https://proceedings.mlr.press/v78/lopez-guevara17a.html | [
"Tatiana Lopez-Guevara",
"Nicholas K Taylor",
"Michael U Gutmann",
"Subramanian Ramamoorthy",
"Kartic Subr"
] | null | null | Humans manipulate fluids intuitively using intuitive approximations of the underlying physical model. In this paper, we explore a general methodology that robots may use to develop and improve strategies for overcoming manipulation tasks associated with appropriately defined loss functions. We focus on the specific tas... | [] | null | null | null | null |
v78/smith17a | Improved Adversarial Systems for 3D Object Generation and Reconstruction | https://proceedings.mlr.press/v78/smith17a.html | [
"Edward J. Smith",
"David Meger"
] | null | null | This paper describes a new approach for training generative adversarial networks (GAN) to understand the detailed 3D shape of objects. While GANs have been used in this domain previously, they are notoriously hard to train, especially for the complex joint data distribution over 3D objects of many categories and orient... | [] | null | null | 1707.09557 | title_snapshot |
v78/iraji17a | Principal Variety Analysis | https://proceedings.mlr.press/v78/iraji17a.html | [
"Reza Iraji",
"Hamidreza Chitsaz"
] | null | null | We introduce a novel computational framework, Principal Variety Analysis (PVA), for primarily nonlinear data modeling. PVA accommodates algebraic sets as the target subspace through which limitations of other existing approaches is dealt with. PVA power is demonstrated in this paper for learning kinematics of objects, ... | [] | null | null | null | null |
v78/rana17a | Towards Robust Skill Generalization: Unifying Learning from Demonstration and Motion Planning | https://proceedings.mlr.press/v78/rana17a.html | [
"Muhammad Asif Rana",
"Mustafa Mukadam",
"Seyed Reza Ahmadzadeh",
"Sonia Chernova",
"Byron Boots"
] | null | null | In this paper, we present Combined Learning from demonstration And Motion Planning (CLAMP) as an efficient approach to skill learning and generalizable skill reproduction. CLAMP combines the strengths of Learning from Demonstration (LfD) and motion planning into a unifying framework. We carry out probabilistic inferenc... | [] | null | null | null | null |
v78/jang17a | End-to-End Learning of Semantic Grasping | https://proceedings.mlr.press/v78/jang17a.html | [
"Eric Jang",
"Sudheendra Vijayanarasimhan",
"Peter Pastor",
"Julian Ibarz",
"Sergey Levine"
] | null | null | We consider the task of semantic robotic grasping, in which a robot picks up an object of a user-specified class using only monocular images. Inspired by the two-stream hypothesis of visual reasoning, we present a semantic grasping framework that learns object detection, classification, and grasp planning in an end-to-... | [] | null | null | 1707.01932 | title_snapshot |
v78/drews17a | Aggressive Deep Driving: Combining Convolutional Neural Networks and Model Predictive Control | https://proceedings.mlr.press/v78/drews17a.html | [
"Paul Drews",
"Grady Williams",
"Brian Goldfain",
"Evangelos A. Theodorou",
"James M. Rehg"
] | null | null | We present a framework for vision-based model predictive control (MPC) for the task of aggressive, high-speed autonomous driving. Our approach uses deep convolutional neural networks to predict cost functions from input video which are directly suitable for online trajectory optimization with MPC. We demonstrate the me... | [] | null | null | null | null |
v78/laskey17a | DART: Noise Injection for Robust Imitation Learning | https://proceedings.mlr.press/v78/laskey17a.html | [
"Michael Laskey",
"Jonathan Lee",
"Roy Fox",
"Anca Dragan",
"Ken Goldberg"
] | null | null | One approach to Imitation Learning is Behavior Cloning, in which a robot observes a supervisor and infers a control policy. A known problem with this “off-policy" approach is that the robot’s errors compound when drifting away from the supervisor’s demonstrations. On-policy, techniques alleviate this by iteratively c... | [] | null | null | 1703.09327 | title_snapshot |
v78/erickson17a | Semi-Supervised Haptic Material Recognition for Robots using Generative Adversarial Networks | https://proceedings.mlr.press/v78/erickson17a.html | [
"Zackory Erickson",
"Sonia Chernova",
"Charles C. Kemp"
] | null | null | Material recognition enables robots to incorporate knowledge of material properties into their interactions with everyday objects. For example, material recognition opens up opportunities for clearer communication with a robot, such as "bring me the metal coffee mug", and recognizing plastic versus metal is crucial whe... | [] | null | null | 1707.02796 | title_snapshot |
v78/tanneberg17a | Online Learning with Stochastic Recurrent Neural Networks using Intrinsic Motivation Signals | https://proceedings.mlr.press/v78/tanneberg17a.html | [
"Daniel Tanneberg",
"Jan Peters",
"Elmar Rueckert"
] | null | null | Continuous online adaptation is an essential ability for the vision of fully autonomous and lifelong-learning robots. Robots need to be able to adapt to changing environments and constraints while this adaption should be performed without interrupting the robot’s motion. In this paper, we introduce a framework for prob... | [] | null | null | null | null |
v78/medina17a | Learning Stable Task Sequences from Demonstration with Linear Parameter Varying Systems and Hidden Markov Models | https://proceedings.mlr.press/v78/medina17a.html | [
"Jose R. Medina",
"Aude Billard"
] | null | null | The problem of acquiring multiple tasks from demonstration is typically divided in two sequential processes: (1) the segmentation or identification of different subgoals/subtasks and (2) a separate learning process that parameterizes a control policy for each subtask. As a result, segmentation criteria typically neglec... | [] | null | null | null | null |
v78/gao17a | Intention-Net: Integrating Planning and Deep Learning for Goal-Directed Autonomous Navigation | https://proceedings.mlr.press/v78/gao17a.html | [
"Wei Gao",
"David Hsu",
"Wee Sun Lee",
"Shengmei Shen",
"Karthikk Subramanian"
] | null | null | How can a delivery robot navigate reliably to a destination in a new office building, with minimal prior information? To tackle this challenge, this paper introduces a two-level hierarchical approach, which integrates model-free deep learning and model-based path planning. At the low level, a neural-network motion cont... | [] | null | null | 1710.05627 | title_snapshot |
v78/kalweit17a | Uncertainty-driven Imagination for Continuous Deep Reinforcement Learning | https://proceedings.mlr.press/v78/kalweit17a.html | [
"Gabriel Kalweit",
"Joschka Boedecker"
] | null | null | Continuous control of high-dimensional systems can be achieved by current state-of-the-art reinforcement learning methods such as the Deep Deterministic Policy Gradient algorithm, but needs a significant amount of data samples. For real-world systems, this can be an obstacle since excessive data collection can be expen... | [] | null | null | null | null |
v78/cabi17a | The Intentional Unintentional Agent: Learning to Solve Many Continuous Control Tasks Simultaneously | https://proceedings.mlr.press/v78/cabi17a.html | [
"Serkan Cabi",
"Sergio Gómez Colmenarejo",
"Matthew W. Hoffman",
"Misha Denil",
"Ziyu Wang",
"Nando Freitas"
] | null | null | This paper introduces the Intentional Unintentional (IU) agent. This agent endows the deep deterministic policy gradients (DDPG) agent for continuous control with the ability to solve several tasks simultaneously. Learning to solve many tasks simultaneously has been a long-standing, core goal of artificial intelligence... | [] | null | null | 1707.03300 | title_snapshot |
v78/bajcsy17a | Learning Robot Objectives from Physical Human Interaction | https://proceedings.mlr.press/v78/bajcsy17a.html | [
"Andrea Bajcsy",
"Dylan P. Losey",
"Marcia K. O’Malley",
"Anca D. Dragan"
] | null | null | When humans and robots work in close proximity, physical interaction is inevitable. Traditionally, robots treat physical interaction as a disturbance, and resume their original behavior after the interaction ends. In contrast, we argue that physical human interaction is informative: it is useful information about how ... | [] | null | null | null | null |
v78/doerr17a | Optimizing Long-term Predictions for Model-based Policy Search | https://proceedings.mlr.press/v78/doerr17a.html | [
"Andreas Doerr",
"CHristian Daniel",
"Duy Nguyen-Tuong",
"Alonso Marco",
"Stefan Schaal",
"Toussaint Marc",
"Sebastian Trimpe"
] | null | null | We propose a novel long-term optimization criterion to improve the robustness of model-based reinforcement learning in real-world scenarios. Learning a dynamics model to derive a solution promises much greater data-efficiency and reusability compared to model-free alternatives. In practice, however, modelbased RL suffe... | [] | null | null | null | null |
v78/schenck17a | Learning Robotic Manipulation of Granular Media | https://proceedings.mlr.press/v78/schenck17a.html | [
"Connor Schenck",
"Jonathan Tompson",
"Sergey Levine",
"Dieter Fox"
] | null | null | In this paper, we examine the problem of robotic manipulation of granular media. We evaluate multiple predictive models used to infer the dynamics of scooping and dumping actions. These models are evaluated on a task that involves manipulating the media in order to deform it into a desired shape. Our best performing m... | [] | null | null | 1709.02833 | title_snapshot |
v78/liu17a | Learning End-to-end Multimodal Sensor Policies for Autonomous Navigation | https://proceedings.mlr.press/v78/liu17a.html | [
"Guan-Horng Liu",
"Avinash Siravuru",
"Sai Prabhakar",
"Manuela Veloso",
"George Kantor"
] | null | null | We proposed a multimodal end-to-end policy based on deep reinforcement learning (DRL) that leverages sensor fusion to reduced performance drops in noisy environment from 50% to 10% compared with the baseline and makes the policy functional even in the face of partial sensor failure by using a novel stochastic technique... | [] | null | null | 1705.10422 | title_snapshot |
v78/rusu17a | Sim-to-Real Robot Learning from Pixels with Progressive Nets | https://proceedings.mlr.press/v78/rusu17a.html | [
"Andrei A. Rusu",
"Matej Večerík",
"Thomas Rothörl",
"Nicolas Heess",
"Razvan Pascanu",
"Raia Hadsell"
] | null | null | Applying end-to-end learning to solve complex, interactive, pixel-driven control tasks on a robot is an unsolved problem. Deep Reinforcement Learning algorithms are too slow to achieve performance on a real robot, but their potential has been demonstrated in simulated environments. We propose using \emphprogressive net... | [] | null | null | 1610.04286 | title_snapshot |
v78/bhardwaj17a | Learning Heuristic Search via Imitation | https://proceedings.mlr.press/v78/bhardwaj17a.html | [
"Mohak Bhardwaj",
"Sanjiban Choudhury",
"Sebastian Scherer"
] | null | null | Robotic motion planning problems are typically solved by constructing a search tree of valid maneuvers from a start to a goal configuration. Limited onboard computation and real-time planning constraints impose a limit on how large this search tree can grow. Heuristics play a crucial role in such situations by guiding ... | [] | null | null | 1707.03034 | title_snapshot |
v78/wulfmeier17a | Mutual Alignment Transfer Learning | https://proceedings.mlr.press/v78/wulfmeier17a.html | [
"Markus Wulfmeier",
"Ingmar Posner",
"Pieter Abbeel"
] | null | null | Training robots for operation in the real world is a complex, time consuming and potentially expensive task. Despite significant success of reinforcement learning in games and simulations, research in real robot applications has not been able to match similar progress. While sample complexity can be reduced by trainin... | [] | null | null | 1707.07907 | title_snapshot |
v78/viereck17a | Learning a visuomotor controller for real world robotic grasping using simulated depth images | https://proceedings.mlr.press/v78/viereck17a.html | [
"Ulrich Viereck",
"Andreas Pas",
"Kate Saenko",
"Robert Platt"
] | null | null | We want to build robots that are useful in unstructured real world applications, such as doing work in the household. Grasping in particular is an important skill in this domain, yet it remains a challenge. One of the key hurdles is handling unexpected changes or motion in the objects being grasped and kinematic noise ... | [] | null | null | 1706.04652 | title_snapshot |
v78/klimek17a | Hierarchical Reinforcement Learning with Parameters | https://proceedings.mlr.press/v78/klimek17a.html | [
"Maciej Klimek",
"Henryk Michalewski",
"Piotr Mi\\loś"
] | null | null | In this work we introduce and evaluate a model of Hierarchical Reinforcement Learning with Parameters. In the first stage we train agents to execute relatively simple actions like reaching or gripping. In the second stage we train a hierarchical manager to compose these actions to solve more complicated tasks. The man... | [] | null | null | null | null |
v78/calandra17a | The Feeling of Success: Does Touch Sensing Help Predict Grasp Outcomes? | https://proceedings.mlr.press/v78/calandra17a.html | [
"Roberto Calandra",
"Andrew Owens",
"Manu Upadhyaya",
"Wenzhen Yuan",
"Justin Lin",
"Edward H. Adelson",
"Sergey Levine"
] | null | null | A successful grasp requires careful balancing of the contact forces. Deducing whether a particular grasp will be successful from indirect measurements, such as vision, is therefore quite challenging, and direct sensing of contacts through touch sensing provides an appealing avenue toward more successful and consistent... | [] | null | null | 1710.05512 | title_snapshot |
v78/standley17a | image2mass: Estimating the Mass of an Object from Its Image | https://proceedings.mlr.press/v78/standley17a.html | [
"Trevor Standley",
"Ozan Sener",
"Dawn Chen",
"Silvio Savarese"
] | null | null | Successful robotic manipulation of real-world objects requires an understanding of the physical properties of these objects. We propose a model for estimating one such physical property, mass, from an object’s image. We collect a large dataset of online product information containing images, sizes, and weights. We com... | [] | null | null | null | null |
v78/james17a | Transferring End-to-End Visuomotor Control from Simulation to Real World for a Multi-Stage Task | https://proceedings.mlr.press/v78/james17a.html | [
"Stephen James",
"Andrew J. Davison",
"Edward Johns"
] | null | null | End-to-end control for robot manipulation and grasping is emerging as an attractive alternative to traditional pipelined approaches. However, end-to-end methods tend to either be slow to train, exhibit little or no generalisability, or lack the ability to accomplish long-horizon or multi-stage tasks. In this paper, we ... | [] | null | null | 1707.02267 | title_snapshot |
v78/frederik-ebert17a | Self-Supervised Visual Planning with Temporal Skip Connections | https://proceedings.mlr.press/v78/frederik-ebert17a.html | [
"Frederik Ebert",
"Chelsea Finn",
"Alex X. Lee",
"Sergey Levine"
] | null | null | In order to autonomously learn wide repertoires of complex skills, robots must be able to learn from their own autonomously collected data, without human supervision. One learning signal that is always available for autonomously collected data is prediction. If a robot can learn to predict the future, it can use this p... | [] | null | null | 1710.05268 | title_snapshot |
v78/finn17a | One-Shot Visual Imitation Learning via Meta-Learning | https://proceedings.mlr.press/v78/finn17a.html | [
"Chelsea Finn",
"Tianhe Yu",
"Tianhao Zhang",
"Pieter Abbeel",
"Sergey Levine"
] | null | null | In order for a robot to be a generalist that can perform a wide range of jobs, it must be able to acquire a wide variety of skills quickly and efficiently in complex unstructured environments. High-capacity models such as deep neural networks can enable a robot to represent complex skills, but learning each skill from ... | [] | null | null | 1709.04905 | title_snapshot |
v78/ravichandar17a | Learning Partially Contracting Dynamical Systems from Demonstrations | https://proceedings.mlr.press/v78/ravichandar17a.html | [
"Harish Ravichandar",
"Iman Salehi",
"Ashwin Dani"
] | null | null | An algorithm for learning the dynamics of point-to-point motions from demonstrations using an autonomous nonlinear dynamical system, named contracting dynamical system primitives (CDSP), is presented. The motion dynamics are approximated using a Gaussian mixture model (GMM) and its parameters are learned subject to con... | [] | null | null | null | null |
v78/campbell17a | Bayesian Interaction Primitives: A SLAM Approach to Human-Robot Interaction | https://proceedings.mlr.press/v78/campbell17a.html | [
"Joseph Campbell",
"Heni Ben Amor"
] | null | null | This paper introduces a fully Bayesian reformulation of Interaction Primitives for human-robot interaction and collaboration. A key insight is that a subset of human-robot interaction is conceptually related to simultaneous localization and mapping techniques. Leveraging this insight we can significantly increase the a... | [] | null | null | null | null |
v78/fazeli17a | Learning Data-Efficient Rigid-Body Contact Models: Case Study of Planar Impact | https://proceedings.mlr.press/v78/fazeli17a.html | [
"Nima Fazeli",
"Samuel Zapolsky",
"Evan Drumwright",
"Alberto Rodriguez"
] | null | null | In this paper we demonstrate the limitations of common rigid-body contact models used in the robotics community by comparing them to a collection of data-driven and data-reinforced models that exploit underlying structure inspired by the rigid contact paradigm. We evaluate and compare the analytical and data-driven co... | [] | null | null | 1710.05947 | title_snapshot |
v78/wu17a | Emergent Behaviors in Mixed-Autonomy Traffic | https://proceedings.mlr.press/v78/wu17a.html | [
"Cathy Wu",
"Aboudy Kreidieh",
"Eugene Vinitsky",
"Alexandre M. Bayen"
] | null | null | Traffic dynamics are often modeled by complex dynamical systems for which classical analysis tools can struggle to provide tractable policies used by transportation agencies and planners. In light of the introduction of automated vehicles into transportation systems, there is a new need for understanding the impacts of... | [] | null | null | null | null |
v78/wang17a | How Robots Learn to Classify New Objects Trained from Small Data Sets | https://proceedings.mlr.press/v78/wang17a.html | [
"Tick Son Wang",
"Zoltan-Csaba Marton",
"Manuel Brucker",
"Rudolph Triebel"
] | null | null | In this paper, we address the problem of learning to classify new object classes and instances by adapting a previously trained classifier. The main challenges here are the small amount of newly available training data and the large change in appearance between the new and the old data. To address this we propose a new... | [] | null | null | null | null |
v78/krishnan17a | DDCO: Discovery of Deep Continuous Options for Robot Learning from Demonstrations | https://proceedings.mlr.press/v78/krishnan17a.html | [
"Sanjay Krishnan",
"Roy Fox",
"Ion Stoica",
"Ken Goldberg"
] | null | null | An option is a short-term skill consisting of a control policy for a specified region of the state space, and a termination condition recognizing leaving that region. In prior work, we proposed an algorithm called Deep Discovery of Options (DDO) to discover options to accelerate reinforcement learning in Atari games. T... | [] | null | null | 1710.05421 | title_snapshot |
v78/martin17a | Extending Model-based Policy Gradients for Robots in Heteroscedastic Environments | https://proceedings.mlr.press/v78/martin17a.html | [
"John Martin",
"Brendan Englot"
] | null | null | In this paper, we consider the problem of learning robot control policies in heteroscedastic environments, whose noise properties vary throughout a robot’s state and action space. We consider reinforcement learning algorithms that evaluate policies using learned models of the environment, and we extend this class of al... | [] | null | null | null | null |
v78/shukla17a | Learning Human Utility from Video Demonstrations for Deductive Planning in Robotics | https://proceedings.mlr.press/v78/shukla17a.html | [
"Nishant Shukla",
"Yunzhong He",
"Frank Chen",
"Song-Chun Zhu"
] | null | null | We uncouple three components of autonomous behavior (utilitarian value, causal reasoning, and fine motion control) to design an interpretable model of tasks from video demonstrations. Utilitarian value is learned from aggregating human preferences to understand the implicit goal of a task, explaining \textitwhy an acti... | [] | null | null | null | null |
v78/senanayake17a | Bayesian Hilbert Maps for Dynamic Continuous Occupancy Mapping | https://proceedings.mlr.press/v78/senanayake17a.html | [
"Ransalu Senanayake",
"Fabio Ramos"
] | null | null | Hilbert mapping is an efficient technique for building continuous occupancy maps from depth sensors such as LiDAR in static environments. However, to make the map adaptable to dynamic environments, its parameters need to be learned automatically. In this paper, we take a variational Bayesian approach to this problem, t... | [] | null | null | null | null |
v78/whitman17a | Learning Dynamics Across Similar Spatiotemporally-Evolving Physical Systems | https://proceedings.mlr.press/v78/whitman17a.html | [
"Joshua Whitman",
"Girish Chowdhary"
] | null | null | We present a differentially-constrained machine learning model that can generalize over similar spatiotemporally evolving dynamical systems. It is shown that not only can an E-GP model be used to estimate the latent state of large-scale physical systems of this type, but that a single E-GP model can generalize over mul... | [] | null | null | null | null |
v78/florensa17a | Reverse Curriculum Generation for Reinforcement Learning | https://proceedings.mlr.press/v78/florensa17a.html | [
"Carlos Florensa",
"David Held",
"Markus Wulfmeier",
"Michael Zhang",
"Pieter Abbeel"
] | null | null | Many relevant tasks require an agent to reach a certain state, or to manipulate objects into a desired configuration. For example, we might want a robot to align and assemble a gear onto an axle or insert and turn a key in a lock. These goal-oriented tasks present a considerable challenge for reinforcement learning, si... | [] | null | null | 1707.05300 | title_snapshot |
v78/das17a | Fastron: An Online Learning-Based Model and Active Learning Strategy for Proxy Collision Detection | https://proceedings.mlr.press/v78/das17a.html | [
"Nikhil Das",
"Naman Gupta",
"Michael Yip"
] | null | null | We introduce the Fastron, a configuration space (C-space) model to be used as a proxy to kinematic-based collision detection. The Fastron allows iterative updates to account for a changing environment through a combination of a novel formulation of the kernel perceptron learning algorithm and an active learning strateg... | [] | null | null | 1709.02316 | title_snapshot |
v78/ebrahimi17a | Gradient-free Policy Architecture Search and Adaptation | https://proceedings.mlr.press/v78/ebrahimi17a.html | [
"Sayna Ebrahimi",
"Anna Rohrbach",
"Trevor Darrell"
] | null | null | We develop a method for policy architecture search and adaptation via gradient-free optimization which can learn to perform autonomous driving tasks. By learning from both demonstration and environmental reward we develop a model that can learn with relatively few early catastrophic failures. We first learn an archi... | [] | null | null | 1710.05958 | title_snapshot |
v78/mahler17a | Learning Deep Policies for Robot Bin Picking by Simulating Robust Grasping Sequences | https://proceedings.mlr.press/v78/mahler17a.html | [
"Jeffrey Mahler",
"Ken Goldberg"
] | null | null | Recent results suggest that it is possible to grasp a variety of singulated objects with high precision using Convolutional Neural Networks (CNNs) trained on synthetic data. This paper considers the task of bin picking, where multiple objects are randomly arranged in a heap and the objective is to sequentially grasp an... | [] | null | null | null | null |
v78/fulda17a | Harvesting Common-sense Navigational Knowledge for Robotics from Uncurated Text Corpora | https://proceedings.mlr.press/v78/fulda17a.html | [
"Nancy Fulda",
"Nathan Tibbetts",
"Zachary Brown",
"David Wingate"
] | null | null | As robotic systems are deployed into everyday situations, the need for abstract reasoning becomes more pronounced. The ideal robotic assistant should be able to understand verbal commands and work independently to fulfill human-prescribed goals, even if instructions are ambiguous or circumstances change. This paper pre... | [] | null | null | null | null |
README.md exists but content is empty.
- Downloads last month
- 55