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S. LaValle, “Rapidly-exploring random trees: A new tool for path planning,” Research Report 9811 , 1998
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M. Vecerik, T. Hester, J. Scholz, F. Wang, O. Pietquin, B. Piot, N. Heess, T. Rothörl, T. Lampe, and M. Riedmiller, “Leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards,” arXiv preprint arXiv:1707.08817 , 2017
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2017
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A. Rajeswaran, V. Kumar, A. Gupta, G. Vezzani, J. Schulman, E. Todorov, and S. Levine, “Learning complex dexterous manipulation with deep reinforcement learning and demonstrations,” arXiv preprint arXiv:1709.10087 , 2017
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2017
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J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, “Proximal policy optimization algorithms,” arXiv preprint arXiv:1707.06347 , 2017
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2017
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J. Tobin, R. Fong, A. Ray, J. Schneider, W. Zaremba, and P. Abbeel, “Domain randomization for transferring deep neural networks from simulation to the real world,” in 2017 IEEE/RSJ international conference on intelligent robots and systems (IROS) . IEEE, 2017, pp. 23–30
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M. T. Mason, “Toward robotic manipulation,” Annual Review of Control, Robotics, and Autonomous Systems , vol. 1, pp. 1–28, 2018
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C. Rognon, S. Mintchev, F. Dell’Agnola, D. Atienza, and D. Floreano, “Flyjacket: An upper body soft exoskeleton for immersive drone control.” IEEE, 2018, pp. 2362–2369
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X. B. Peng, P. Abbeel, S. Levine, and M. Van de Panne, “Deepmimic: Example-guided deep reinforcement learning of physics-based character skills,” ACM Transactions On Graphics (TOG) , vol. 37, no. 4, pp. 1–14, 2018
2018
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A. Nair, B. McGrew, M. Andrychowicz, W. Zaremba, and P. Abbeel, “Overcoming exploration in reinforcement learning with demonstrations,” in 2018 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2018, pp. 6292–6299
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Y. Zhu, Z. Wang, J. Merel, A. Rusu, T. Erez, S. Cabi, S. Tunyasuvunakool, J. Kramár, R. Hadsell, N. de Freitas et al. , “Reinforcement and imitation learning for diverse visuomotor skills,” arXiv preprint arXiv:1802.09564 , 2018
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2018
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H. Zhu, A. Gupta, A. Rajeswaran, S. Levine, and V. Kumar, “Dexterous manipulation with deep reinforcement learning: Efficient, general, and low-cost,” in 2019 International Conference on Robotics and Automation (ICRA) . IEEE, 2019, pp. 3651–3657
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V. G. Goecks, G. M. Gremillion, V. J. Lawhern, J. Valasek, and N. R. Waytowich, “Integrating behavior cloning and reinforcement learning for improved performance in dense and sparse reward environments,” arXiv preprint arXiv:1910.04281 , 2019
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2019
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S. Christen, S. Stevšić, and O. Hilliges, “Demonstration-guided deep reinforcement learning of control policies for dexterous human-robot interaction,” in 2019 International Conference on Robotics and Automation (ICRA) . IEEE, 2019, pp. 2161–2167
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R. Tedrake and the Drake Development Team, “Drake: Model-based design and verification for robotics,” 2019. [Online]. Available: https://drake.mit.edu
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N. Chavan-Dafle and A. Rodriguez, “Sampling-based planning of in-hand manipulation with external pushes,” in Robotics Research: The 18th International Symposium ISRR . Springer, 2020, pp. 523–539
2020
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A. Ö. Önol, R. Corcodel, P. Long, and T. Padır, “Tuning-free contact-implicit trajectory optimization,” in 2020 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2020, pp. 1183–1189
2020
Earlier work this paper cites.

Similar

O. M. Andrychowicz, B. Baker, M. Chociej, R. Jozefowicz, B. McGrew, J. Pachocki, A. Petron, M. Plappert, G. Powell, A. Ray et al. , “Learning dexterous in-hand manipulation,” The International Journal of Robotics Research , vol. 39, no. 1, pp. 3–20, 2020
2020
Cited alongside, same era.
A. Nagabandi, K. Konolige, S. Levine, and V. Kumar, “Deep dynamics models for learning dexterous manipulation,” in Conference on Robot Learning . PMLR, 2020, pp. 1101–1112
2020
Cited alongside, same era.
X. B. Peng, E. Coumans, T. Zhang, T.-W. Lee, J. Tan, and S. Levine, “Learning agile robotic locomotion skills by imitating animals,” arXiv preprint arXiv:2004.00784 , 2020
Original
2020
Cited alongside, same era.
A. Nair, A. Gupta, M. Dalal, and S. Levine, “AWAC: Accelerating online reinforcement learning with offline datasets,” arXiv preprint arXiv:2006.09359 , 2020
Original
2020
Cited alongside, same era.

Then

A. Goncalves, N. Kuppuswamy, A. Beaulieu, A. Uttamchandani, K. M. Tsui, and A. Alspach, “Punyo-1: Soft tactile-sensing upper-body robot for large object manipulation and physical human interaction,” in 2022 IEEE 5th International Conference on Soft Robotics (RoboSoft) . IEEE, 2022, pp. 844–851
2022
Later among the works it cites.
M. Zhang, D. K. Jha, A. U. Raghunathan, and K. Hauser, “Simultaneous trajectory optimization and contact selection for multi-modal manipulation planning,” arXiv preprint arXiv:2306.06465 , 2023
Original
2023
Closest in time.
R. Natarajan, G. L. Johnston, N. Simaan, M. Likhachev, and H. Choset, “Torque-limited manipulation planning through contact by interleaving graph search and trajectory optimization,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 8148–8154
2023
Closest in time.
C. Chi, S. Feng, Y. Du, Z. Xu, E. Cousineau, B. Burchfiel, and S. Song, “Diffusion policy: Visuomotor policy learning via action diffusion,” arXiv preprint arXiv:2303.04137 , 2023

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2023

Plan-Guided Reinforcement Learning for Whole-Body Manipulation

Zhang, Mengchao, Barreiros, Jose, Onol, Aykut Ozgun

Understand

Synthesizing complex whole-body manipulation behaviors has fundamental challenges due to the rapidly growing combinatorics inherent to contact interaction planning.

While model-based methods have shown promising results in solving long-horizon manipulation tasks, they often work under strict assumptions, such as known model parameters, oracular observation of the environment state, and simplified dynamics, resulting in plans that cannot easily transfer to hardware.
Learning-based approaches, such as imitation learning (IL) and reinforcement learning (RL), have been shown to be robust when operating over in-distribution states; however, they need heavy human supervision.
Specifically, model-free RL requires a tedious reward-shaping process.

Built on

S. LaValle, “Rapidly-exploring random trees: A new tool for path planning,” Research Report 9811 , 1998
1998
Earlier work this paper cites.
M. Vecerik, T. Hester, J. Scholz, F. Wang, O. Pietquin, B. Piot, N. Heess, T. Rothörl, T. Lampe, and M. Riedmiller, “Leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards,” arXiv preprint arXiv:1707.08817 , 2017
Original
2017
Earlier work this paper cites.
A. Rajeswaran, V. Kumar, A. Gupta, G. Vezzani, J. Schulman, E. Todorov, and S. Levine, “Learning complex dexterous manipulation with deep reinforcement learning and demonstrations,” arXiv preprint arXiv:1709.10087 , 2017
Original
2017
Earlier work this paper cites.
J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, “Proximal policy optimization algorithms,” arXiv preprint arXiv:1707.06347 , 2017
Original
2017
Earlier work this paper cites.
J. Tobin, R. Fong, A. Ray, J. Schneider, W. Zaremba, and P. Abbeel, “Domain randomization for transferring deep neural networks from simulation to the real world,” in 2017 IEEE/RSJ international conference on intelligent robots and systems (IROS) . IEEE, 2017, pp. 23–30
2017
Earlier work this paper cites.
M. T. Mason, “Toward robotic manipulation,” Annual Review of Control, Robotics, and Autonomous Systems , vol. 1, pp. 1–28, 2018
2018
Earlier work this paper cites.
C. Rognon, S. Mintchev, F. Dell’Agnola, D. Atienza, and D. Floreano, “Flyjacket: An upper body soft exoskeleton for immersive drone control.” IEEE, 2018, pp. 2362–2369
2018
Earlier work this paper cites.
X. B. Peng, P. Abbeel, S. Levine, and M. Van de Panne, “Deepmimic: Example-guided deep reinforcement learning of physics-based character skills,” ACM Transactions On Graphics (TOG) , vol. 37, no. 4, pp. 1–14, 2018
2018
Earlier work this paper cites.
A. Nair, B. McGrew, M. Andrychowicz, W. Zaremba, and P. Abbeel, “Overcoming exploration in reinforcement learning with demonstrations,” in 2018 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2018, pp. 6292–6299
2018
Earlier work this paper cites.
Y. Zhu, Z. Wang, J. Merel, A. Rusu, T. Erez, S. Cabi, S. Tunyasuvunakool, J. Kramár, R. Hadsell, N. de Freitas et al. , “Reinforcement and imitation learning for diverse visuomotor skills,” arXiv preprint arXiv:1802.09564 , 2018
Original
2018
Earlier work this paper cites.
H. Zhu, A. Gupta, A. Rajeswaran, S. Levine, and V. Kumar, “Dexterous manipulation with deep reinforcement learning: Efficient, general, and low-cost,” in 2019 International Conference on Robotics and Automation (ICRA) . IEEE, 2019, pp. 3651–3657
2019
Earlier work this paper cites.
V. G. Goecks, G. M. Gremillion, V. J. Lawhern, J. Valasek, and N. R. Waytowich, “Integrating behavior cloning and reinforcement learning for improved performance in dense and sparse reward environments,” arXiv preprint arXiv:1910.04281 , 2019
Original
2019
Earlier work this paper cites.
S. Christen, S. Stevšić, and O. Hilliges, “Demonstration-guided deep reinforcement learning of control policies for dexterous human-robot interaction,” in 2019 International Conference on Robotics and Automation (ICRA) . IEEE, 2019, pp. 2161–2167
2019
Earlier work this paper cites.
R. Tedrake and the Drake Development Team, “Drake: Model-based design and verification for robotics,” 2019. [Online]. Available: https://drake.mit.edu
2019
Earlier work this paper cites.
N. Chavan-Dafle and A. Rodriguez, “Sampling-based planning of in-hand manipulation with external pushes,” in Robotics Research: The 18th International Symposium ISRR . Springer, 2020, pp. 523–539
2020
Earlier work this paper cites.
A. Ö. Önol, R. Corcodel, P. Long, and T. Padır, “Tuning-free contact-implicit trajectory optimization,” in 2020 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2020, pp. 1183–1189
2020
Earlier work this paper cites.

Similar

O. M. Andrychowicz, B. Baker, M. Chociej, R. Jozefowicz, B. McGrew, J. Pachocki, A. Petron, M. Plappert, G. Powell, A. Ray et al. , “Learning dexterous in-hand manipulation,” The International Journal of Robotics Research , vol. 39, no. 1, pp. 3–20, 2020
2020
Cited alongside, same era.
A. Nagabandi, K. Konolige, S. Levine, and V. Kumar, “Deep dynamics models for learning dexterous manipulation,” in Conference on Robot Learning . PMLR, 2020, pp. 1101–1112
2020
Cited alongside, same era.
X. B. Peng, E. Coumans, T. Zhang, T.-W. Lee, J. Tan, and S. Levine, “Learning agile robotic locomotion skills by imitating animals,” arXiv preprint arXiv:2004.00784 , 2020
Original
2020
Cited alongside, same era.
A. Nair, A. Gupta, M. Dalal, and S. Levine, “AWAC: Accelerating online reinforcement learning with offline datasets,” arXiv preprint arXiv:2006.09359 , 2020
Original
2020
Cited alongside, same era.

Then

A. Goncalves, N. Kuppuswamy, A. Beaulieu, A. Uttamchandani, K. M. Tsui, and A. Alspach, “Punyo-1: Soft tactile-sensing upper-body robot for large object manipulation and physical human interaction,” in 2022 IEEE 5th International Conference on Soft Robotics (RoboSoft) . IEEE, 2022, pp. 844–851
2022
Later among the works it cites.
M. Zhang, D. K. Jha, A. U. Raghunathan, and K. Hauser, “Simultaneous trajectory optimization and contact selection for multi-modal manipulation planning,” arXiv preprint arXiv:2306.06465 , 2023
Original
2023
Closest in time.
R. Natarajan, G. L. Johnston, N. Simaan, M. Likhachev, and H. Choset, “Torque-limited manipulation planning through contact by interleaving graph search and trajectory optimization,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 8148–8154
2023
Closest in time.
C. Chi, S. Feng, Y. Du, Z. Xu, E. Cousineau, B. Burchfiel, and S. Song, “Diffusion policy: Visuomotor policy learning via action diffusion,” arXiv preprint arXiv:2303.04137 , 2023

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

N. Kuppuswamy, A. Alspach, A. Uttamchandani, S. Creasey, T. Ikeda, and R. Tedrake, “Soft-bubble grippers for robust and perceptive manipulation,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2020, pp. 9917–9924

2020

Cited alongside, same era.

X. B. Peng, Z. Ma, P. Abbeel, S. Levine, and A. Kanazawa, “Amp: Adversarial motion priors for stylized physics-based character control,” ACM Transactions on Graphics (ToG) , vol. 40, no. 4, pp. 1–20, 2021

2021

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V. Makoviychuk, L. Wawrzyniak, Y. Guo, M. Lu, K. Storey, M. Macklin, D. Hoeller, N. Rudin, A. Allshire, A. Handa, and G. State, “Isaac gym: High performance gpu-based physics simulation for robot learning,” 2021

2021

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T. Pang, H. Suh, L. Yang, and R. Tedrake, “Global planning for contact-rich manipulation via local smoothing of quasi-dynamic contact models,” arXiv preprint arXiv:2206.10787 , 2022

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X. Cheng, E. Huang, Y. Hou, and M. T. Mason, “Contact mode guided motion planning for quasidynamic dexterous manipulation in 3D,” in 2022 International Conference on Robotics and Automation (ICRA) . IEEE, 2022, pp. 2730–2736

2022

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M. Wang, A. Ö. Önol, P. Long, and T. Padır, “Contact-implicit planning and control for non-prehensile manipulation using state-triggered constraints,” in The International Symposium of Robotics Research . Springer, 2022, pp. 189–204

2022

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J. Barreiros, L. Tianshu, M. Chiaramonte, K. Jost, Y. Menguc, N. Colonnese, and P. Agarwal, “Hyfar: A textile soft actuator for haptic clothing interfaces.” ACM, 2022

2022

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T. Chen, J. Xu, and P. Agrawal, “A system for general in-hand object re-orientation,” in Conference on Robot Learning . PMLR, 2022, pp. 297–307

2022

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2023

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S. Haldar, J. Pari, A. Rai, and L. Pinto, “Teach a robot to fish: Versatile imitation from one minute of demonstrations,” arXiv preprint arXiv:2303.01497 , 2023

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M. Du, S. Nair, D. Sadigh, and C. Finn, “Behavior retrieval: Few-shot imitation learning by querying unlabeled datasets,” arXiv preprint arXiv:2304.08742 , 2023

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T. Chen, M. Tippur, S. Wu, V. Kumar, E. Adelson, and P. Agrawal, “Visual dexterity: In-hand dexterous manipulation from depth,” in ICML Workshop on New Frontiers in Learning, Control, and Dynamical Systems , 2023

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S. P. Arunachalam, S. Silwal, B. Evans, and L. Pinto, “Dexterous imitation made easy: A learning-based framework for efficient dexterous manipulation,” in 2023 ieee international conference on robotics and automation (icra) . IEEE, 2023, pp. 5954–5961

2023

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E. Vollenweider, M. Bjelonic, V. Klemm, N. Rudin, J. Lee, and M. Hutter, “Advanced skills through multiple adversarial motion priors in reinforcement learning,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 5120–5126

2023

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C. Li, M. Vlastelica, S. Blaes, J. Frey, F. Grimminger, and G. Martius, “Learning agile skills via adversarial imitation of rough partial demonstrations,” in Conference on Robot Learning . PMLR, 2023, pp. 342–352

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C. Li, S. Blaes, P. Kolev, M. Vlastelica, J. Frey, and G. Martius, “Versatile skill control via self-supervised adversarial imitation of unlabeled mixed motions,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 2944–2950

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J. Wu, G. Xin, C. Qi, and Y. Xue, “Learning robust and agile legged locomotion using adversarial motion priors,” IEEE Robotics and Automation Letters , 2023

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Y. Fuchioka, Z. Xie, and M. Van de Panne, “Opt-mimic: Imitation of optimized trajectories for dynamic quadruped behaviors,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 5092–5098

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A. Miller, S. Fahmi, M. Chignoli, and S. Kim, “Reinforcement learning for legged robots: Motion imitation from model-based optimal control,” arXiv preprint arXiv:2305.10989 , 2023

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D. Kang, J. Cheng, M. Zamora, F. Zargarbashi, and S. Coros, “Rl+ model-based control: Using on-demand optimal control to learn versatile legged locomotion,” arXiv preprint arXiv:2305.17842 , 2023

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S. Huang, Z. Wang, P. Li, B. Jia, T. Liu, Y. Zhu, W. Liang, and S.-C. Zhu, “Diffusion-based generation, optimization, and planning in 3d scenes,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 16 750–16 761

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