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Large Language Models (LLMs) have been shown to be capable of performing high-level planning for long-horizon robotics tasks, yet existing methods require access to a pre-defined skill library (e.g.
Strips: A new approach to the application of theorem proving to problem solving
Richard E Fikes and Nils J Nilsson · 1971
Earlier work this paper cites.
The mathematics of coordinated control of prosthetic arms and manipulators
Daniel E Whitney · 1972
Earlier work this paper cites.
Robot manipulators: mathematics, programming, and control: the computer control of robot manipulators
Richard P Paul · 1981
Earlier work this paper cites.
Dynamics of manipulation robots: theory and application
Miomir Vukobratović and Veljko Potkonjak · 1982
Earlier work this paper cites.
Automatic synthesis of fine-motion strategies for robots
Tomas Lozano-Perez, Matthew T Mason, and Russell H Taylor · 1984
Earlier work this paper cites.
A unified approach for motion and force control of robot manipulators: The operational space formulation
Oussama Khatib · 1987
Earlier work this paper cites.
Sensor-based manipulation planning as a game with nature
Russ H Taylor, Matthew T Mason, and Kenneth Y Goldberg · 1987
Earlier work this paper cites.
Reinforcement learning with hierarchies of machines
Ronald Parr and Stuart Russell · 1997
Earlier work this paper cites.
Between mdps and semi-mdps: A framework for temporal abstraction in reinforcement learning
Richard S Sutton, Doina Precup, and Satinder Singh · 1999
Earlier work this paper cites.
RRT-Connect: An efficient approach to single-query path planning
James J Kuffner Jr. and Steven M LaValle · 2000
Earlier work this paper cites.
Mechanics of robotic manipulation
Matthew T Mason · 2001
Earlier work this paper cites.
Graspit! a versatile simulator for robotic grasping
Andrew T Miller and Peter K Allen · 2004
Earlier work this paper cites.
Mechanical assemblies: their design, manufacture, and role in product development , volume 1
Daniel E Whitney · 2004
Earlier work this paper cites.
Search-based planning for manipulation with motion primitives
Benjamin Cohen, Sachin Chitta, and Maxim Likhachev · 2010
Earlier work this paper cites.
Mujoco: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
Earlier work this paper cites.
Integrated task and motion planning in belief space
Leslie Pack Kaelbling and Tomás Lozano-Pérez · 2013
Earlier work this paper cites.
Finding locally optimal, collision-free trajectories with sequential convex optimization
John Schulman, Jonathan Ho, Alex X Lee, Ibrahim Awwal, Henry Bradlow, and Pieter Abbeel · 2013
Earlier work this paper cites.
Dex-net 1.0: A cloud-based network of 3d objects for robust grasp planning using a multi-armed bandit model with correlated rewards
Jeffrey Mahler, Florian T Pokorny, Brian Hou, Melrose Roderick, Michael Laskey, Mathieu Aubry, Kai Kohlhoff, Torsten Kröger, James Kuffner, and Ken Goldberg · 2016
Earlier work this paper cites.
Scalable deep reinforcement learning for vision-based robotic manipulation
Dmitry Kalashnikov, Alex Irpan, Peter Pastor, Julian Ibarz, Alexander Herzog, Eric Jang, Deirdre Quillen, Ethan Holly, Mrinal Kalakrishnan, Vincent Vanhoucke, et al · 2018
Earlier work this paper cites.
Real-time perception meets reactive motion generation
Daniel Kappler, Franziska Meier, Jan Issac, Jim Mainprice, Cristina Garcia Cifuentes, Manuel Wüthrich, Vincent Berenz, Stefan Schaal, Nathan Ratliff, and Jeannette Bohg · 2018
Earlier work this paper cites.
Data-efficient hierarchical reinforcement learning
Ofir Nachum, Shixiang Shane Gu, Honglak Lee, and Sergey Levine · 2018
Earlier work this paper cites.
Solving rubik’s cube with a robot hand
Ilge Akkaya, Marcin Andrychowicz, Maciek Chociej, Mateusz Litwin, Bob McGrew, Arthur Petron, Alex Paino, Matthias Plappert, Glenn Powell, Raphael Ribas, et al · 2019
Earlier work this paper cites.
Relay policy learning: Solving long-horizon tasks via imitation and reinforcement learning
Abhishek Gupta, Vikash Kumar, Corey Lynch, Sergey Levine, and Karol Hausman · 2019
Earlier work this paper cites.
Dream to control: Learning behaviors by latent imagination
Danijar Hafner, Timothy Lillicrap, Jimmy Ba, and Mohammad Norouzi · 2019
Earlier work this paper cites.
6-dof graspnet: Variational grasp generation for object manipulation
Arsalan Mousavian, Clemens Eppner, and Dieter Fox · 2019
Cited alongside, same era.
Introduction to AI robotics
Robin R Murphy · 2019
Cited alongside, same era.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
Cited alongside, same era.
Language as a cognitive tool to imagine goals in curiosity driven exploration
Cédric Colas, Tristan Karch, Nicolas Lair, Jean-Michel Dussoux, Clément Moulin-Frier, Peter Dominey, and Pierre-Yves Oudeyer · 2020
Cited alongside, same era.
D4rl: Datasets for deep data-driven reinforcement learning
Justin Fu, Aviral Kumar, Ofir Nachum, George Tucker, and Sergey Levine · 2020
Cited alongside, same era.
Megapose: 6d pose estimation of novel objects via render & compare
Yann Labbé, Lucas Manuelli, Arsalan Mousavian, Stephen Tyree, Stan Birchfield, Jonathan Tremblay, Justin Carpentier, Mathieu Aubry, Dieter Fox, and Josef Sivic · 2022
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Distilling motion planner augmented policies into visual control policies for robot manipulation
I-Chun Arthur Liu, Shagun Uppal, Gaurav S Sukhatme, Joseph J Lim, Peter Englert, and Youngwoon Lee · 2022
Later among the works it cites.
Detecting twenty-thousand classes using image-level supervision
Xingyi Zhou, Rohit Girdhar, Armand Joulin, Philipp Krähenbühl, and Ishan Misra · 2022
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Legged locomotion in challenging terrains using egocentric vision
Ananye Agarwal, Ashish Kumar, Jitendra Malik, and Deepak Pathak · 2023
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Affordances from human videos as a versatile representation for robotics
Shikhar Bahl, Russell Mendonca, Lili Chen, Unnat Jain, and Deepak Pathak · 2023
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Guided uncertainty-aware policy optimization: Combining learning and model-based strategies for sample-efficient policy learning
Michelle A Lee, Carlos Florensa, Jonathan Tremblay, Nathan Ratliff, Animesh Garg, Fabio Ramos, and Dieter Fox · 2020
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Adaptively informed trees (ait): Fast asymptotically optimal path planning through adaptive heuristics
Marlin P Strub and Jonathan D Gammell · 2020
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Relmogen: Leveraging motion generation in reinforcement learning for mobile manipulation
Fei Xia, Chengshu Li, Roberto Martín-Martín, Or Litany, Alexander Toshev, and Silvio Savarese · 2020
Cited alongside, same era.
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Tianhe Yu, Deirdre Quillen, Zhanpeng He, Ryan Julian, Karol Hausman, Chelsea Finn, and Sergey Levine · 2020
Cited alongside, same era.
robosuite: A modular simulation framework and benchmark for robot learning
Yuke Zhu, Josiah Wong, Ajay Mandlekar, Roberto Martín-Martín, Abhishek Joshi, Soroush Nasiriany, and Yifeng Zhu · 2020
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Accelerating robotic reinforcement learning via parameterized action primitives
Murtaza Dalal, Deepak Pathak, and Russ R Salakhutdinov · 2021
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Imitating task and motion planning with visuomotor transformers
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Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan-Yen Lo, et al · 2023
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Chan Hee Song, Jiaman Wu, Clayton Washington, Brian M Sadler, Wei-Lun Chao, and Yu Su · 2023
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Saytap: Language to quadrupedal locomotion
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Wenhao Yu, Nimrod Gileadi, Chuyuan Fu, Sean Kirmani, Kuang-Huei Lee, Montse Gonzalez Arenas, Hao-Tien Lewis Chiang, Tom Erez, Leonard Hasenclever, Jan Humplik, Brian Ichter, Ted Xiao, Peng Xu, Andy Zeng, Tingnan Zhang, Nicolas Heess, Dorsa Sadigh, Jie Tan, Yuval Tassa, and Fei Xia · 2023
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