Built on

D. Chen, B. Zhou, V. Koltun, and P. Krähenbühl, “Learning by cheating,” CoRR , vol. abs/1912.12294, 2019. [Online]. Available: http://arxiv.org/abs/1912.12294
Original
1912
Earlier work this paper cites.
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Köpf, E. Z. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, “Pytorch: An imperative style, high-performance deep learning library,” CoRR , vol. abs/1912.01703, 2019. [Online]. Available: http://arxiv.org/abs/1912.01703
Original
1912
Earlier work this paper cites.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation , vol. 9, no. 8, pp. 1735–1780, 1997
1997
Earlier work this paper cites.
J. Siekmann, S. Valluri, J. Dao, L. Bermillo, H. Duan, A. Fern, and J. W. Hurst, “Learning memory-based control for human-scale bipedal locomotion,” CoRR , vol. abs/2006.02402, 2020. [Online]. Available: https://arxiv.org/abs/2006.02402
Original
2006
Earlier work this paper cites.
A. Lagae, S. Lefebvre, R. Cook, T. DeRose, G. Drettakis, D. Ebert, J. Lewis, K. Perlin, and M. Zwicker, “A survey of procedural noise functions,” Computer Graphics Forum , vol. 29, no. 8, pp. 2579–2600, 2010. [Online]. Available: https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1467-8659.2010.01827.x
2010
Earlier work this paper cites.
E. Todorov, T. Erez, and Y. Tassa, “Mujoco: A physics engine for model-based control,” in 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems , 2012, pp. 5026–5033
2012
Earlier work this paper cites.
X. B. Peng, “Developing locomotion skills with deep reinforcement learning,” Ph.D. dissertation, University of British Columbia, 2017. [Online]. Available: https://open.library.ubc.ca/collections/ubctheses/24/items/1.0345638
2017
Earlier work this paper cites.
J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, “Proximal policy optimization algorithms,” 2017. [Online]. Available: https://arxiv.org/abs/1707.06347
Original
2017
Earlier work this paper cites.

Similar

X. B. Peng and M. van de Panne, “Learning locomotion skills using deeprl: Does the choice of action space matter?” in Proceedings of the ACM SIGGRAPH / Eurographics Symposium on Computer Animation , ser. SCA ’17. New York, NY, USA: Association for Computing Machinery, 2017. [Online]. Available: https://doi.org/10.1145/3099564.3099567
2017
Cited alongside, same era.
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) , 2017, pp. 23–30
2017
Cited alongside, same era.
R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction , 2nd ed. The MIT Press, 2018. [Online]. Available: http://incompleteideas.net/book/the-book-2nd.html
2018
Cited alongside, same era.
W. Yu, G. Turk, and C. K. Liu, “Learning symmetric and low-energy locomotion,” ACM Trans. Graph. , vol. 37, no. 4, jul 2018. [Online]. Available: https://doi.org/10.1145/3197517.3201397

Then

J. Lee, J. Hwangbo, L. Wellhausen, V. Koltun, and M. Hutter, “Learning quadrupedal locomotion over challenging terrain,” Science Robotics , vol. 5, no. 47, oct 2020. [Online]. Available: https://doi.org/10.1126%2Fscirobotics.abc5986
2020
Later among the works it cites.
Z. Li, X. Cheng, X. B. Peng, P. Abbeel, S. Levine, G. Berseth, and K. Sreenath, “Reinforcement learning for robust parameterized locomotion control of bipedal robots,” CoRR , vol. abs/2103.14295, 2021. [Online]. Available: https://arxiv.org/abs/2103.14295
Original
2021
Later among the works it cites.
J. Siekmann, Y. Godse, A. Fern, and J. Hurst, “Sim-to-real learning of all common bipedal gaits via periodic reward composition,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) , 2021, pp. 7309–7315
2021
Later among the works it cites.
J. Siekmann, K. Green, J. Warila, A. Fern, and J. Hurst, “Blind Bipedal Stair Traversal via Sim-to-Real Reinforcement Learning,” in Proceedings of Robotics: Science and Systems , Virtual, July 2021
2021

Beyond the bibliography

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

Open on alphaXiv

alphaXiv is searching for related work…

Reading the bibliography…

2023

Learning Perceptive Bipedal Locomotion over Irregular Terrain

van Marum, Bart, Sabatelli, Matthia, Kasaei, Hamidreza

Understand

In this paper we propose a novel bipedal locomotion controller that uses noisy exteroception to traverse a wide variety of terrains.

Building on the cutting-edge advancements in attention based belief encoding for quadrupedal locomotion, our work extends these methods to the bipedal domain, resulting in a robust and reliable internal belief of the terrain ahead despite noisy sensor inputs.
Additionally, we present a reward function that allows the controller to successfully traverse irregular terrain.
We compare our method with a proprioceptive baseline and show that our method is able to traverse a wide variety of terrains and greatly outperforms the state-of-the-art in terms of robustness, speed and efficiency.

Built on

D. Chen, B. Zhou, V. Koltun, and P. Krähenbühl, “Learning by cheating,” CoRR , vol. abs/1912.12294, 2019. [Online]. Available: http://arxiv.org/abs/1912.12294
Original
1912
Earlier work this paper cites.
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Köpf, E. Z. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, “Pytorch: An imperative style, high-performance deep learning library,” CoRR , vol. abs/1912.01703, 2019. [Online]. Available: http://arxiv.org/abs/1912.01703
Original
1912
Earlier work this paper cites.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation , vol. 9, no. 8, pp. 1735–1780, 1997
1997
Earlier work this paper cites.
J. Siekmann, S. Valluri, J. Dao, L. Bermillo, H. Duan, A. Fern, and J. W. Hurst, “Learning memory-based control for human-scale bipedal locomotion,” CoRR , vol. abs/2006.02402, 2020. [Online]. Available: https://arxiv.org/abs/2006.02402
Original
2006
Earlier work this paper cites.
A. Lagae, S. Lefebvre, R. Cook, T. DeRose, G. Drettakis, D. Ebert, J. Lewis, K. Perlin, and M. Zwicker, “A survey of procedural noise functions,” Computer Graphics Forum , vol. 29, no. 8, pp. 2579–2600, 2010. [Online]. Available: https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1467-8659.2010.01827.x
2010
Earlier work this paper cites.
E. Todorov, T. Erez, and Y. Tassa, “Mujoco: A physics engine for model-based control,” in 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems , 2012, pp. 5026–5033
2012
Earlier work this paper cites.
X. B. Peng, “Developing locomotion skills with deep reinforcement learning,” Ph.D. dissertation, University of British Columbia, 2017. [Online]. Available: https://open.library.ubc.ca/collections/ubctheses/24/items/1.0345638
2017
Earlier work this paper cites.
J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, “Proximal policy optimization algorithms,” 2017. [Online]. Available: https://arxiv.org/abs/1707.06347
Original
2017
Earlier work this paper cites.

Similar

X. B. Peng and M. van de Panne, “Learning locomotion skills using deeprl: Does the choice of action space matter?” in Proceedings of the ACM SIGGRAPH / Eurographics Symposium on Computer Animation , ser. SCA ’17. New York, NY, USA: Association for Computing Machinery, 2017. [Online]. Available: https://doi.org/10.1145/3099564.3099567
2017
Cited alongside, same era.
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) , 2017, pp. 23–30
2017
Cited alongside, same era.
R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction , 2nd ed. The MIT Press, 2018. [Online]. Available: http://incompleteideas.net/book/the-book-2nd.html
2018
Cited alongside, same era.
W. Yu, G. Turk, and C. K. Liu, “Learning symmetric and low-energy locomotion,” ACM Trans. Graph. , vol. 37, no. 4, jul 2018. [Online]. Available: https://doi.org/10.1145/3197517.3201397

Then

J. Lee, J. Hwangbo, L. Wellhausen, V. Koltun, and M. Hutter, “Learning quadrupedal locomotion over challenging terrain,” Science Robotics , vol. 5, no. 47, oct 2020. [Online]. Available: https://doi.org/10.1126%2Fscirobotics.abc5986
2020
Later among the works it cites.
Z. Li, X. Cheng, X. B. Peng, P. Abbeel, S. Levine, G. Berseth, and K. Sreenath, “Reinforcement learning for robust parameterized locomotion control of bipedal robots,” CoRR , vol. abs/2103.14295, 2021. [Online]. Available: https://arxiv.org/abs/2103.14295
Original
2021
Later among the works it cites.
J. Siekmann, Y. Godse, A. Fern, and J. Hurst, “Sim-to-real learning of all common bipedal gaits via periodic reward composition,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) , 2021, pp. 7309–7315
2021
Later among the works it cites.
J. Siekmann, K. Green, J. Warila, A. Fern, and J. Hurst, “Blind Bipedal Stair Traversal via Sim-to-Real Reinforcement Learning,” in Proceedings of Robotics: Science and Systems , Virtual, July 2021
2021

Beyond the bibliography

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

Open on alphaXiv

alphaXiv is searching for related work…

2018

Cited alongside, same era.

Z. Xie, G. Berseth, P. Clary, J. W. Hurst, and M. van de Panne, “Feedback control for cassie with deep reinforcement learning,” CoRR , vol. abs/1803.05580, 2018. [Online]. Available: http://arxiv.org/abs/1803.05580

Original

2018

Cited alongside, same era.

Agility Robotics, “cassie-mujoco-sim,” GitHub repository , 2018. [Online]. Available: https://github.com/osudrl/cassie-mujoco-sim

2018

Cited alongside, same era.

J. Reher, W.-L. Ma, and A. D. Ames, “Dynamic walking with compliance on a cassie bipedal robot,” in 2019 18th European Control Conference (ECC) , 2019, pp. 2589–2595

2019

Cited alongside, same era.

Y. Gong, R. Hartley, X. Da, A. Hereid, O. Harib, J.-K. Huang, and J. Grizzle, “Feedback control of a cassie bipedal robot: Walking, standing, and riding a segway,” in 2019 American Control Conference (ACC) , 2019, pp. 4559–4566

2019

Cited alongside, same era.