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We introduce Habitat 2.0 (H2.0), a simulation platform for training virtual robots in interactive 3D environments and complex physics-enabled scenarios.
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Objectnav revisited: On evaluation of embodied agents navigating to objects
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R Smith · 2009
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Chomp: Gradient optimization techniques for efficient motion planning
Nathan Ratliff, Matt Zucker, J Andrew Bagnell, and Siddhartha Srinivasa · 2009
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Ros: an open-source robot operating system
Morgan Quigley, Ken Conley, Brian Gerkey, Josh Faust, Tully Foote, Jeremy Leibs, Rob Wheeler, and Andrew Y Ng · 2009
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Integrated task and motion planning
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Autonomous door opening and plugging in with a personal robot
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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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Rearrangement: A challenge for embodied AI
Dhruv Batra, Angel X Chang, Sonia Chernova, Andrew J Davison, Jia Deng, Vladlen Koltun, Sergey Levine, Jitendra Malik, Igor Mordatch, Roozbeh Mottaghi, Manolis Savva, and Hao Su · 2011
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Task space regions: A framework for pose-constrained manipulation planning
Dmitry Berenson, Siddhartha Srinivasa, and James Kuffner · 2011
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How to train pointgoal navigation agents on a (sample and compute) budget
Erik Wijmans, Irfan Essa, and Dhruv Batra · 2012
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Mujoco: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
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Ioan A Sucan, Mark Moll, and Lydia E Kavraki · 2012
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Data-driven grasp synthesis—a survey
Jeannette Bohg, Antonio Morales, Tamim Asfour, and Danica Kragic · 2013
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The arcade learning environment: An evaluation platform for general agents
Marc G Bellemare, Yavar Naddaf, Joel Veness, and Michael Bowling · 2013
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CHRONO: A parallel multi-physics library for rigid-body, flexible-body, and fluid dynamics
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Whole-body motion planning for manipulation of articulated objects
Felix Burget, Armin Hornung, and Maren Bennewitz · 2013
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Dart: Dense articulated real-time tracking
Tanner Schmidt, Richard A Newcombe, and Dieter Fox · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Reducing the barrier to entry of complex robotic software: a moveit! case study
David Coleman, Ioan Sucan, Sachin Chitta, and Nikolaus Correll · 2014
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Motion planning with sequential convex optimization and convex collision checking
John Schulman, Yan Duan, Jonathan Ho, Alex Lee, Ibrahim Awwal, Henry Bradlow, Jia Pan, Sachin Patil, Ken Goldberg, and Pieter Abbeel · 2014
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Informed rrt*: Optimal sampling-based path planning focused via direct sampling of an admissible ellipsoidal heuristic
Jonathan D Gammell, Siddhartha S Srinivasa, and Timothy D Barfoot · 2014
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The YCB object and model set: Towards common benchmarks for manipulation research
Berk Calli, Arjun Singh, Aaron Walsman, Siddhartha Srinivasa, Pieter Abbeel, and Aaron M Dollar · 2015
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Batch informed trees (bit*): Sampling-based optimal planning via the heuristically guided search of implicit random geometric graphs
Jonathan D Gammell, Siddhartha S Srinivasa, and Timothy D Barfoot · 2015
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Hierarchical fingertip space: A unified framework for grasp planning and in-hand grasp adaptation
Kaiyu Hang, Miao Li, Johannes A Stork, Yasemin Bekiroglu, Florian T Pokorny, Aude Billard, and Danica Kragic · 2016
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Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
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Team delft’s robot winner of the amazon picking challenge 2016
Carlos Hernandez, Mukunda Bharatheesha, Wilson Ko, Hans Gaiser, Jethro Tan, Kanter van Deurzen, Maarten de Vries, Bas Van Mil, Jeff van Egmond, Ruben Burger, et al · 2016
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AI2-Thor: An interactive 3D environment for visual AI
Eric Kolve, Roozbeh Mottaghi, Winson Han, Eli VanderBilt, Luca Weihs, Alvaro Herrasti, Daniel Gordon, Yuke Zhu, Abhinav Gupta, and Ali Farhadi · 2017
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MINOS: Multimodal indoor simulator for navigation in complex environments
Manolis Savva, Angel X. Chang, Alexey Dosovitskiy, Thomas Funkhouser, and Vladlen Koltun · 2017
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Grad-cam: Visual explanations from deep networks via gradient-based localization
6-dof grasping for target-driven object manipulation in clutter
Adithyavairavan Murali, Arsalan Mousavian, Clemens Eppner, Chris Paxton, and Dieter Fox · 2020
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SAPIEN: A simulated part-based interactive environment
Fanbo Xiang, Yuzhe Qin, Kaichun Mo, Yikuan Xia, Hao Zhu, Fangchen Liu, Minghua Liu, Hanxiao Jiang, Yifu Yuan, He Wang, Li Yi, Angel X. Chang, Leonidas J. Guibas, and Hao Su · 2020
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Rlbench: The robot learning benchmark & learning environment
Stephen James, Zicong Ma, David Rovick Arrojo, and Andrew J Davison · 2020
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iGibson, a simulation environment for interactive tasks in large realistic scenes
Bokui Shen, Fei Xia, Chengshu Li, Roberto Martın-Martın, Linxi Fan, Guanzhi Wang, Shyamal Buch, Claudia D’Arpino, Sanjana Srivastava, Lyne P Tchapmi, Kent Vainio, Li Fei-Fei, and Silvio Savarese · 2020
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Pyrender
Matthew Matl · 2020
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Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2017
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Yuval Tassa, Yotam Doron, Alistair Muldal, Tom Erez, Yazhe Li, Diego de Las Casas, David Budden, Abbas Abdolmaleki, Josh Merel, Andrew Lefrancq, et al · 2018
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Dart: Dynamic animation and robotics toolkit
Jeongseok Lee, Michael X Grey, Sehoon Ha, Tobias Kunz, Sumit Jain, Yuting Ye, Siddhartha S Srinivasa, Mike Stilman, and C Karen Liu · 2018
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Building generalizable agents with a realistic and rich 3d environment
Yi Wu, Yuxin Wu, Georgia Gkioxari, and Yuandong Tian · 2018
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Chalet: Cornell house agent learning environment
Claudia Yan, Dipendra Misra, Andrew Bennnett, Aaron Walsman, Yonatan Bisk, and Yoav Artzi · 2018
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VirtualHome: Simulating household activities via programs
Xavier Puig, Kevin Ra, Marko Boben, Jiaman Li, Tingwu Wang, Sanja Fidler, and Antonio Torralba · 2018
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Mapping instructions to actions in 3d environments with visual goal prediction
Dipendra Misra, Andrew Bennett, Valts Blukis, Eyvind Niklasson, Max Shatkhin, and Yoav Artzi · 2018
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Sim-to-real: Learning agile locomotion for quadruped robots
Jie Tan, Tingnan Zhang, Erwin Coumans, Atil Iscen, Yunfei Bai, Danijar Hafner, Steven Bohez, and Vincent Vanhoucke · 2018
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Fei Xia, William B Shen, Chengshu Li, Priya Kasimbeg, Micael Edmond Tchapmi, Alexander Toshev, Roberto Martín-Martín, and Silvio Savarese · 2020
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Alfred: A benchmark for interpreting grounded instructions for everyday tasks
Mohit Shridhar, Jesse Thomason, Daniel Gordon, Yonatan Bisk, Winson Han, Roozbeh Mottaghi, Luke Zettlemoyer, and Dieter Fox · 2020
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Sim2real predictivity: Does evaluation in simulation predict real-world performance?
Abhishek Kadian, Joanne Truong, Aaron Gokaslan, Alexander Clegg, Erik Wijmans, Stefan Lee, Manolis Savva, Sonia Chernova, and Dhruv Batra · 2020
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Sim2real transfer for deep reinforcement learning with stochastic state transition delays
Sandeep Singh Sandha, Luis Garcia, Bharathan Balaji, Fatima M Anwar, and Mani Srivastava · 2020
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An empirical investigation of the challenges of real-world reinforcement learning
Gabriel Dulac-Arnold, Nir Levine, Daniel J. Mankowitz, Jerry Li, Cosmin Paduraru, Sven Gowal, and Todd Hester · 2020
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Basis universal
Binomial LLC · 2020
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Hand-motion-guided articulation and segmentation estimation
Richard Sahala Hartanto, Ryoichi Ishikawa, Menandro Roxas, and Takeshi Oishi · 2020
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Accelerating reinforcement learning through gpu atari emulation
Steven Dalton, Iuri Frosio, and Michael Garland · 2020
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Sample factory: Egocentric 3D control from pixels at 100000 FPS with asynchronous reinforcement learning
Aleksei Petrenko, Zhehui Huang, Tushar Kumar, Gaurav Sukhatme, and Vladlen Koltun · 2020
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Posterior sampling for anytime motion planning on graphs with expensive-to-evaluate edges
Brian Hou, Sanjiban Choudhury, Gilwoo Lee, Aditya Mandalika, and Siddhartha S Srinivasa · 2020
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Fahad Islam, Chris Paxton, Clemens Eppner, Bryan Peele, Maxim Likhachev, and Dieter Fox · 2020
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Differentiable gaussian process motion planning
Mohak Bhardwaj, Byron Boots, and Mustafa Mukadam · 2020
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Exploratory not explanatory: Counterfactual analysis of saliency maps for deep reinforcement learning
Akanksha Atrey, Kaleigh Clary, and David Jensen · 2020
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Learning to Rearrange Deformable Cables, Fabrics, and Bags with Goal-Conditioned Transporter Networks
Daniel Seita, Pete Florence, Jonathan Tompson, Erwin Coumans, Vikas Sindhwani, Ken Goldberg, and Andy Zeng · 2021
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