Fetching the paper…

Reading the bibliography…

2023

Learning Vision-Based Bipedal Locomotion for Challenging Terrain

Duan, Helei, Pandit, Bikram, Gadde, Mohitvishnu S. et al.

Original
PDF
Post on X

Understand

Reinforcement learning (RL) for bipedal locomotion has recently demonstrated robust gaits over moderate terrains using only proprioceptive sensing.

However, such blind controllers will fail in environments where robots must anticipate and adapt to local terrain, which requires visual perception.
In this paper, we propose a fully-learned system that allows bipedal robots to react to local terrain while maintaining commanded travel speed and direction.
Our approach first trains a controller in simulation using a heightmap expressed in the robot's local frame.

Learning Vision-Based Bipedal Locomotion for Challenging Terrain · Around

Built on

R. J. Griffin, G. Wiedebach, S. McCrory, S. Bertrand, I. Lee, and J. Pratt, “Footstep Planning for Autonomous Walking over Rough Terrain,” IEEE-RAS International Conference on Humanoid Robots , vol. 2019-Octob, pp. 9–16, 7 2019. [Online]. Available: http://arxiv.org/abs/1907.08673
Original
1907
Earlier work this paper cites.
J. J. GIBSON, “Visually Controlled Locomotion and Visual Orientation in Animals,” British Journal of Psychology , vol. 49, no. 3, pp. 182–194, 8 1958. [Online]. Available: https://onlinelibrary.wiley.com/doi/10.1111/j.2044-8295.1958.tb00656.x
1958
Earlier work this paper cites.
C. Plagemann, S. Mischke, S. Prentice, K. Kersting, N. Roy, and W. Burgard, “A bayesian regression approach to terrain mapping and an application to legged robor locomotion,” Journal of Field Robotics , vol. 26, no. 10, pp. 789–811, 10 2009
2009
Earlier work this paper cites.
J. Siekmann, Y. Godse, A. Fern, and J. Hurst, “Sim-to-Real Learning of All Common Bipedal Gaits via Periodic Reward Composition,” 2021, pp. 7309–7315. [Online]. Available: http://arxiv.org/abs/2011.01387
Original
2011
Earlier work this paper cites.
S. Gangapurwala, M. Geisert, R. Orsolino, M. Fallon, and I. Havoutis, “RLOC: Terrain-Aware Legged Locomotion Using Reinforcement Learning and Optimal Control,” IEEE Transactions on Robotics , vol. 38, no. 5, pp. 2908–2927, 2022. [Online]. Available: http://arxiv.org/abs/2012.03094
Original
2012
Earlier work this paper cites.
E. Todorov, T. Erez, and Y. Tassa, “MuJoCo: A physics engine for model-based control,” IEEE International Conference on Intelligent Robots and Systems , pp. 5026–5033, 2012
2012
Earlier work this paper cites.
J. S. Matthis and B. R. Fajen, “Humans exploit the biomechanics of bipedal gait during visually guided walking over complex terrain,” Proceedings of the Royal Society B: Biological Sciences , vol. 280, no. 1762, 2013
2013
Earlier work this paper cites.
R. Deits and R. Tedrake, “Footstep planning on uneven terrain with mixed-integer convex optimization,” IEEE-RAS International Conference on Humanoid Robots , vol. 2015-Febru, pp. 279–286, 2015
2015
Earlier work this paper cites.
O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” MICCAI 2015 , vol. 9351, pp. 234–241, 2015
2015
Earlier work this paper cites.
P. Fankhauser and M. Hutter, “A universal grid map library: Implementation and use case for rough terrain navigation,” Studies in Computational Intelligence , vol. 625, no. October 2017, pp. 99–120, 2016
2016
Earlier work this paper cites.
J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, “Proximal Policy Optimization Algorithms,” pp. 1–12, 2017. [Online]. Available: http://arxiv.org/abs/1707.06347
Original
2017
Earlier work this paper cites.
P. Fankhauser, “Perceptive Locomotion for Legged Robots in Rough Terrain,” ETH Zurich , p. 177, 2018
2018
Earlier work this paper cites.

Similar

P. Fankhauser, M. Bloesch, and M. Hutter, “Probabilistic Terrain Mapping for Mobile Robots With Uncertain Localization,” IEEE Robotics and Automation Letters , vol. 3, no. 4, pp. 3019–3026, 10 2018. [Online]. Available: https://ieeexplore.ieee.org/document/8392399/
2018
Cited alongside, same era.
C. Mastalli, I. Havoutis, M. Focchi, D. G. Caldwell, and C. Semini, “Motion planning for quadrupedal locomotion: Coupled planning, terrain mapping, and whole-body control,” IEEE Transactions on Robotics , vol. 36, no. 6, pp. 1635–1648, 2020
2020
Cited alongside, same era.
F. Jenelten, T. Miki, A. E. Vijayan, M. Bjelonic, and M. Hutter, “Perceptive Locomotion in Rough Terrain - Online Foothold Optimization,” IEEE Robotics and Automation Letters , vol. 5, no. 4, pp. 5370–5376, 2020
2020
Cited alongside, same era.
W. Yu, D. Jain, A. Escontrela, and A. Iscen, “Visual-Locomotion : Learning to Walk on Complex Terrains with Vision,” 5th Conference on Robot Learning (CoRL 2021) , no. CoRL 2021, pp. 1–12, 2021

Then

H. Duan, A. Malik, M. S. Gadde, J. Dao, A. Fern, and J. Hurst, “Learning Dynamic Bipedal Walking Across Stepping Stones,” IEEE International Conference on Intelligent Robots and Systems , vol. 2022-Octob, pp. 6746–6752, 5 2022. [Online]. Available: http://arxiv.org/abs/2205.01807
Original
2022
Later among the works it cites.
N. Rudin, D. Hoeller, P. Reist, and M. Hutter, “Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning,” in Proceedings of the 5th Conference on Robot Learning , ser. Proceedings of Machine Learning Research, A. Faust, D. Hsu, and G. Neumann, Eds., vol. 164. PMLR, 3 2022, pp. 91–100. [Online]. Available: https://proceedings.mlr.press/v164/rudin22a.html
2022
Later among the works it cites.
Z. Fu, A. Kumar, A. Agarwal, H. Qi, J. Malik, and D. Pathak, “Coupling Vision and Proprioception for Navigation of Legged Robots,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition , vol. 2022-June, pp. 17 252–17 262, 2022. [Online]. Available: http://arxiv.org/abs/2112.02094
Original
2022
Later among the works it cites.

Beyond the bibliography

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

Open on alphaXiv

alphaXiv is searching for related work…

2021

Cited alongside, same era.

G. B. Margolis, T. Chen, K. Paigwar, X. Fu, D. Kim, S. Kim, and P. Agrawal, “Learning to Jump from Pixels,” no. CoRL 2021, pp. 1–10, 2021. [Online]. Available: http://arxiv.org/abs/2110.15344

2021

Cited alongside, same era.

J. Siekmann, K. Green, J. Warila, A. Fern, and J. Hurst, “Blind Bipedal Stair Traversal via Sim-to-Real Reinforcement Learning,” Robotics: Science and Systems , 2021

2021

Cited alongside, same era.

G. A. Castillo, B. Weng, W. Zhang, and A. Hereid, “Robust Feedback Motion Policy Design Using Reinforcement Learning on a 3D Digit Bipedal Robot,” IEEE International Conference on Intelligent Robots and Systems , pp. 5136–5143, 2021. [Online]. Available: http://arxiv.org/abs/2103.15309

2021

Cited alongside, same era.

A. Agrawal, S. Chen, A. Rai, and K. Sreenath, “Vision-Aided Dynamic Quadrupedal Locomotion on Discrete Terrain Using Motion Libraries,” Proceedings - IEEE International Conference on Robotics and Automation , pp. 4708–4714, 2022. [Online]. Available: http://arxiv.org/abs/2110.00891

2022

Cited alongside, same era.

T. Miki, J. Lee, J. Hwangbo, L. Wellhausen, V. Koltun, and M. Hutter, “Learning robust perceptive locomotion for quadrupedal robots in the wild,” Science Robotics , vol. 7, no. 62, p. eabk2822, 2022

2022

Cited alongside, same era.

A. Agarwal, A. Kumar, J. Malik, and D. Pathak, “Legged Locomotion in Challenging Terrains using Egocentric Vision,” 11 2022. [Online]. Available: http://arxiv.org/abs/2211.07638

2022

Cited alongside, same era.

N. Rudin, D. Hoeller, M. Bjelonic, and M. Hutter, “Advanced Skills by Learning Locomotion and Local Navigation End-to-End,” IEEE International Conference on Intelligent Robots and Systems , vol. 2022-Octob, pp. 2497–2503, 9 2022. [Online]. Available: http://arxiv.org/abs/2209.12827

2022

Cited alongside, same era.

A. Loquercio, A. Kumar, and J. Malik, “Learning Visual Locomotion with Cross-Modal Supervision,” Tech. Rep., 2022. [Online]. Available: http://arxiv.org/abs/2211.03785

2022

Cited alongside, same era.

D. Hoeller, N. Rudin, C. Choy, A. Anandkumar, and M. Hutter, “Neural Scene Representation for Locomotion on Structured Terrain,” IEEE Robotics and Automation Letters , vol. 7, no. 4, pp. 8667–8674, 6 2022. [Online]. Available: http://arxiv.org/abs/2206.08077

2022

Later among the works it cites.

R. Grandia, F. Jenelten, S. Yang, F. Farshidian, and M. Hutter, “Perceptive Locomotion Through Nonlinear Model-Predictive Control,” IEEE Transactions on Robotics , 8 2023. [Online]. Available: http://arxiv.org/abs/2208.08373

2023

Closest in time.

D. Hoeller, N. Rudin, D. Sako, and M. Hutter, “ANYmal Parkour: Learning Agile Navigation for Quadrupedal Robots,” 6 2023. [Online]. Available: http://arxiv.org/abs/2306.14874

2023

Closest in time.

Z. Li, X. B. Peng, P. Abbeel, S. Levine, G. Berseth, and K. Sreenath, “Robust and Versatile Bipedal Jumping Control through Multi-Task Reinforcement Learning,” 2 2023. [Online]. Available: http://arxiv.org/abs/2302.09450

2023

Closest in time.

B. van Marum, M. Sabatelli, and H. Kasaei, “Learning Perceptive Bipedal Locomotion over Irregular Terrain,” Tech. Rep., 2023. [Online]. Available: http://arxiv.org/abs/2304.07236

2023

Closest in time.

Z. Zhuang, Z. Fu, J. Wang, C. Atkeson, S. Schwertfeger, C. Finn, and H. Zhao, “Robot Parkour Learning,” Tech. Rep., 2023. [Online]. Available: http://arxiv.org/abs/2309.05665

2023

Closest in time.

T. Haarnoja, B. Moran, G. Lever, S. H. Huang, D. Tirumala, M. Wulfmeier, J. Humplik, S. Tunyasuvunakool, N. Y. Siegel, R. Hafner, M. Bloesch, K. Hartikainen, A. Byravan, L. Hasenclever, Y. Tassa, F. Sadeghi, N. Batchelor, F. Casarini, S. Saliceti, C. Game, N. Sreendra, K. Patel, M. Gwira, A. Huber, N. Hurley, F. Nori, R. Hadsell, and N. Heess, “Learning Agile Soccer Skills for a Bipedal Robot with Deep Reinforcement Learning,” 4 2023. [Online]. Available: http://arxiv.org/abs/2304.13653

2023

Closest in time.

B. Yang, Q. Zhang, R. Geng, L. Wang, and M. Liu, “Real-Time Neural Dense Elevation Mapping for Urban Terrain with Uncertainty Estimations,” IEEE Robotics and Automation Letters , vol. 8, no. 2, pp. 696–703, 2 2023

2023

Closest in time.

Y. Ma, F. Farshidian, and M. Hutter, “Learning Arm-Assisted Fall Damage Reduction and Recovery for Legged Mobile Manipulators,” IEEE International Conference on Robotics and Automation, 2023 , 3 2023. [Online]. Available: http://arxiv.org/abs/2303.05486

2023

Closest in time.