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Differentiable SLAM-net: Learning Particle SLAM for Visual Navigation
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Curious Representation Learning for Embodied Intelligence
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ManipulaTHOR: A Framework for Visual Object Manipulation
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Visual Room Rearrangement
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The ThreeDWorld Transport Challenge: A Visually Guided Task-and-Motion Planning Benchmark Towards Physically Realistic Embodied AI
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Success Weighted by Completion Time: A Dynamics-Aware Evaluation Criteria for Embodied Navigation
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Large Batch Simulation for Deep Reinforcement Learning
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Mesh Manifold Based Riemannian Motion Planning for Omnidirectional Micro Aerial Vehicles
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Alternative Paths Planner (APP) for Provably Fixed-time Manipulation Planning in Semi-structured Environments
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On the use of simulation in robotics: Opportunities, challenges, and suggestions for moving forward
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How to Train PointGoal Navigation Agents on a (Sample and Compute) Budget
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Learning to Rearrange Deformable Cables, Fabrics, and Bags with Goal-Conditioned Transporter Networks
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iGibson 1.0: A Simulation Environment for Interactive Tasks in Large Realistic Scenes
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Rearrangement: A Challenge for Embodied AI
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Robot Navigation in Constrained Pedestrian Environments using Reinforcement Learning
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Room-Across-Room: Multilingual Vision-and-Language Navigation with Dense Spatiotemporal Grounding
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Integrated Task and Motion Planning
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Occupancy Anticipation for Efficient Exploration and Navigation
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ReLMoGen: Leveraging Motion Generation in Reinforcement Learning for Mobile Manipulation
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OpenRooms: An Open Framework for Photorealistic Indoor Scene Datasets
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ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation
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Auxiliary Tasks Speed Up Learning PointGoal Navigation
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ObjectNav Revisited: On Evaluation of Embodied Agents Navigating to Objects
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Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement Learning
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A Hand Motion-guided Articulation and Segmentation Estimation
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Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels
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An empirical investigation of the challenges of real-world reinforcement learning
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SAPIEN: A SimulAted Part-Based Interactive ENvironment
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Posterior Sampling for Anytime Motion Planning on Graphs with Expensive-to-Evaluate Edges
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Sim2Real Predictivity: Does Evaluation in Simulation Predict Real-World Performance?
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6-DOF Grasping for Target-driven Object Manipulation in Clutter
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ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Tasks
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Online Replanning in Belief Space for Partially Observable Task and Motion Problems
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DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames
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Interactive Gibson Benchmark: A Benchmark for Interactive Navigation in Cluttered Environments
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RLBench: The Robot Learning Benchmark & Learning Environment
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Exploratory Not Explanatory: Counterfactual Analysis of Saliency Maps for Deep Reinforcement Learning
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rlpyt: A Research Code Base for Deep Reinforcement Learning in PyTorch
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Differentiable Gaussian Process Motion Planning
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Accelerating Reinforcement Learning through GPU Atari Emulation
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ORRB - OpenAI Remote Rendering Backend
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The Replica Dataset: A Digital Replica of Indoor Spaces
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6-DOF GraspNet: Variational Grasp Generation for Object Manipulation
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Habitat: A Platform for Embodied AI Research
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Combining Optimal Control and Learning for Visual Navigation in Novel Environments
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Scan2CAD: Learning CAD Model Alignment in RGB-D Scans
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Sanity Checks for Saliency Maps
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Mapping Instructions to Actions in 3D Environments with Visual Goal Prediction
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On Evaluation of Embodied Navigation Agents
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VirtualHome: Simulating Household Activities Via Programs
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Sampling-Based Methods for Motion Planning with Constraints
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Sim-to-Real: Learning Agile Locomotion For Quadruped Robots
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DART: Dynamic Animation and Robotics Toolkit
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IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
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CHALET: Cornell House Agent Learning Environment
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Building Generalizable Agents with a Realistic and Rich 3D Environment
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AI2-THOR: An Interactive 3D Environment for Visual AI
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MINOS: Multimodal Indoor Simulator for Navigation in Complex Environments
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Vision-and-Language Navigation: Interpreting Visually-Grounded Navigation Instructions in Real Environments
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PRM-RL: Long-range Robotic Navigation Tasks by Combining Reinforcement Learning and Sampling-Based Planning
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Learning Sampling Distributions for Robot Motion Planning
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Continuous-time Gaussian process motion planning via probabilistic inference
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Proximal Policy Optimization Algorithms
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Real-Time Perception Meets Reactive Motion Generation
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Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
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Hierarchical Fingertip Space: A Unified Framework for Grasp Planning and In-Hand Grasp Adaptation
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Team Delft's Robot Winner of the Amazon Picking Challenge 2016
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The YCB object and Model set: Towards common benchmarks for manipulation research
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Adam: A Method for Stochastic Optimization
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Motion planning with sequential convex optimization and convex collision checking
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DART: Dense Articulated Real-Time Tracking
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Batch Informed Trees (BIT*): Sampling-based optimal planning via the heuristically guided search of implicit random geometric graphs
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Reducing the Barrier to Entry of Complex Robotic Software: a MoveIt! Case Study
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Informed RRT*: Optimal sampling-based path planning focused via direct sampling of an admissible ellipsoidal heuristic
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Data-Driven Grasp Synthesis—A Survey
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Whole-body motion planning for manipulation of articulated objects
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CHRONO: a parallel multi-physics library for rigid-body, flexible-body, and fluid dynamics
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MuJoCo: A physics engine for model-based control
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The Open Motion Planning Library
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The Arcade Learning Environment: An Evaluation Platform for General Agents
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Pulling open doors and drawers: Coordinating an omni-directional base and a compliant arm with Equilibrium Point control
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Autonomous door opening and plugging in with a personal robot
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CHOMP: Gradient optimization techniques for efficient motion planning
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Manipulation planning on constraint manifolds
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Motion planning for urban driving using RRT
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RRT-connect: An efficient approach to single-query path planning
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Speed of processing in the human visual system
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STRIPS: A New Approach to the Application of Theorem Proving to Problem Solving
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Sim2Real Transfer for Deep Reinforcement Learning with Stochastic State Transition Delays
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Franka emika specification
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Vladimír Vondruš and contributors
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PyBullet, a Python module for physics simulation for games, robotics and machine learning
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Task space regions: A framework for poseconstrained manipulation planning
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ROS: an open-source Robot Operating System
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The Perception-Action Coupling
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The perception of the visual world.
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Seed rl: Scalable and efficient deep-rl with accelerated central inference
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(a) Did you state the full set of assumptions of all theoretical results
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Did you include the estimated hourly wage paid to participants and the total amount spent on participant compensation
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Did you discuss whether and how consent was obtained from people whose data you're using/curating?
107
Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] Code and setup instructions can be
108
Did you include the full text of instructions given to participants and screenshots
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Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
110
Have you read the ethics review guidelines and ensured that your paper conforms to them
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with respect to the random seed after running experiments multiple times)? [Yes] Results in Sec. 5 are over three random seeds and 600 episodes each
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code, data, models) or curating/releasing new assets... (a) If your work uses existing assets
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Did you describe any potential participant risks, with links to Institutional Review Board (IRB) approvals, if applicable?
115
Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] All methods and training details are described in detail in Sec
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If you used crowdsourcing or conducted research with human subjects
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Habitat 2.0: Training Home Assistants to Rearrange their Habitat
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comparing to with and without the learned termination condition.
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ODE: Open Dynamics Engine
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Did you describe the limitations of your work? [Yes] See second paragraph of Sec
