IKEA Furniture Assembly Environment for Long-Horizon Complex Manipulation Tasks

The IKEA Furniture Assembly Environment is one of the first benchmarks for testing and accelerating the automation of complex manipulation tasks. The environment is designed to advance reinforcement learning from simple toy tasks to complex tasks requiring both long-term planning and sophisticated low-level control. Our environment supports over 80 different furniture models, Sawyer and Baxter robot simulation, and domain randomization. The IKEA Furniture Assembly Environment is a testbed for methods aiming to solve complex manipulation tasks. The environment is publicly available at this https URL

[1]  Silvio Savarese,et al.  Neural Task Programming: Learning to Generalize Across Hierarchical Tasks , 2017, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[2]  Yuval Tassa,et al.  MuJoCo: A physics engine for model-based control , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[3]  Silvio Savarese,et al.  ROBOTURK: A Crowdsourcing Platform for Robotic Skill Learning through Imitation , 2018, CoRL.

[4]  Nando de Freitas,et al.  Reinforcement and Imitation Learning for Diverse Visuomotor Skills , 2018, Robotics: Science and Systems.

[5]  Pieter Abbeel,et al.  DoorGym: A Scalable Door Opening Environment And Baseline Agent , 2019, ArXiv.

[6]  Hyeonwoo Noh,et al.  Neural Program Synthesis from Diverse Demonstration Videos , 2018, ICML.

[7]  Yichen Wei,et al.  Relation Networks for Object Detection , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[8]  Dan Klein,et al.  Modular Multitask Reinforcement Learning with Policy Sketches , 2016, ICML.

[9]  Antonio Torralba,et al.  Parsing IKEA Objects: Fine Pose Estimation , 2013, 2013 IEEE International Conference on Computer Vision.

[10]  Joseph J. Lim,et al.  Composing Complex Skills by Learning Transition Policies , 2018, ICLR.

[11]  Razvan Pascanu,et al.  Deep reinforcement learning with relational inductive biases , 2018, ICLR.

[12]  Yuval Tassa,et al.  DeepMind Control Suite , 2018, ArXiv.

[13]  Sergey Levine,et al.  (CAD)$^2$RL: Real Single-Image Flight without a Single Real Image , 2016, Robotics: Science and Systems.

[14]  Sergey Levine,et al.  Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning , 2019, CoRL.

[15]  Wojciech Zaremba,et al.  Domain randomization for transferring deep neural networks from simulation to the real world , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[16]  Marcin Andrychowicz,et al.  One-Shot Imitation Learning , 2017, NIPS.

[17]  Marc G. Bellemare,et al.  The Arcade Learning Environment: An Evaluation Platform for General Agents , 2012, J. Artif. Intell. Res..

[18]  Ashish Kapoor,et al.  AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles , 2017, FSR.

[19]  Joseph J. Lim,et al.  To Follow or not to Follow: Selective Imitation Learning from Observations , 2019, CoRL.

[20]  Andrew J. Davison,et al.  RLBench: The Robot Learning Benchmark & Learning Environment , 2019, IEEE Robotics and Automation Letters.

[21]  Ruslan Salakhutdinov,et al.  Gated-Attention Architectures for Task-Oriented Language Grounding , 2017, AAAI.

[22]  Matthew Botvinick,et al.  MONet: Unsupervised Scene Decomposition and Representation , 2019, ArXiv.

[23]  Balaraman Ravindran,et al.  EPOpt: Learning Robust Neural Network Policies Using Model Ensembles , 2016, ICLR.

[24]  Germán Ros,et al.  CARLA: An Open Urban Driving Simulator , 2017, CoRL.

[25]  OpenAI Learning Dexterous In-Hand Manipulation. , 2018 .

[26]  Ali Farhadi,et al.  AI2-THOR: An Interactive 3D Environment for Visual AI , 2017, ArXiv.

[27]  Tom Schaul,et al.  StarCraft II: A New Challenge for Reinforcement Learning , 2017, ArXiv.

[28]  Doina Precup,et al.  Between MDPs and Semi-MDPs: A Framework for Temporal Abstraction in Reinforcement Learning , 1999, Artif. Intell..

[29]  Marwan Mattar,et al.  Unity: A General Platform for Intelligent Agents , 2018, ArXiv.

[30]  Sergey Levine,et al.  Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations , 2017, Robotics: Science and Systems.

[31]  Jürgen Schmidhuber,et al.  Neural Expectation Maximization , 2017, NIPS.

[32]  Sergey Levine,et al.  Sim2Real View Invariant Visual Servoing by Recurrent Control , 2017, ArXiv.

[33]  Siddhartha S. Srinivasa,et al.  DART: Dynamic Animation and Robotics Toolkit , 2018, J. Open Source Softw..

[34]  Yevgen Chebotar,et al.  Closing the Sim-to-Real Loop: Adapting Simulation Randomization with Real World Experience , 2018, 2019 International Conference on Robotics and Automation (ICRA).

[35]  Anoop Cherian,et al.  Human Action Forecasting by Learning Task Grammars , 2017, ArXiv.

[36]  Richard S. Sutton,et al.  Dyna, an integrated architecture for learning, planning, and reacting , 1990, SGAR.

[37]  Alexandros Karatzoglou,et al.  RecoGym: A Reinforcement Learning Environment for the problem of Product Recommendation in Online Advertising , 2018, ArXiv.

[38]  Sanja Fidler,et al.  VirtualHome: Simulating Household Activities Via Programs , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[39]  Inman Harvey,et al.  Noise and the Reality Gap: The Use of Simulation in Evolutionary Robotics , 1995, ECAL.

[40]  Erwin Coumans,et al.  Bullet physics simulation , 2015, SIGGRAPH Courses.

[41]  Wojciech Zaremba,et al.  OpenAI Gym , 2016, ArXiv.

[42]  Sergey Levine,et al.  Divide-and-Conquer Reinforcement Learning , 2017, ICLR.

[43]  Silvio Savarese,et al.  SURREAL: Open-Source Reinforcement Learning Framework and Robot Manipulation Benchmark , 2018, CoRL.

[44]  Honglak Lee,et al.  Zero-Shot Task Generalization with Multi-Task Deep Reinforcement Learning , 2017, ICML.

[45]  Jakub W. Pachocki,et al.  Learning dexterous in-hand manipulation , 2018, Int. J. Robotics Res..

[46]  Marcin Andrychowicz,et al.  Hindsight Experience Replay , 2017, NIPS.

[47]  Wojciech Jaskowski,et al.  ViZDoom: A Doom-based AI research platform for visual reinforcement learning , 2016, 2016 IEEE Conference on Computational Intelligence and Games (CIG).