Fetching the paper…
Reading the bibliography…
Legged locomotion has recently achieved remarkable success with the progress of machine learning techniques, especially deep reinforcement learning (RL).
Central pattern generators for locomotion control in animals and robots: a review
Auke Jan Ijspeert · 2008
Earlier work this paper cites.
State estimation for legged robots on unstable and slippery terrain
Michael Bloesch, Christian Gehring, Péter Fankhauser, Marco Hutter, Mark A Hoepflinger, and Roland Siegwart · 2013
Earlier work this paper cites.
Learning phrase representations using rnn encoder-decoder for statistical machine translation
Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio · 2014
Earlier work this paper cites.
Anymal-a highly mobile and dynamic quadrupedal robot
Marco Hutter, Christian Gehring, Dominic Jud, Andreas Lauber, C Dario Bellicoso, Vassilios Tsounis, Jemin Hwangbo, Karen Bodie, Peter Fankhauser, Michael Bloesch, et al · 2016
Earlier work this paper cites.
Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al · 2016
Earlier work this paper cites.
The two-state implicit filter recursive estimation for mobile robots
Michael Bloesch, Michael Burri, Hannes Sommer, Roland Siegwart, and Marco Hutter · 2017
Earlier work this paper cites.
Adversarial attacks on neural network policies
Sandy Huang, Nicolas Papernot, Ian Goodfellow, Yan Duan, and Pieter Abbeel · 2017
Earlier work this paper cites.
Robust adversarial reinforcement learning
Lerrel Pinto, James Davidson, Rahul Sukthankar, and Abhinav Gupta · 2017
Earlier work this paper cites.
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
Earlier work this paper cites.
Domain randomization for transferring deep neural networks from simulation to the real world
Josh Tobin, Rachel Fong, Alex Ray, Jonas Schneider, Wojciech Zaremba, and Pieter Abbeel · 2017
Earlier work this paper cites.
Dynamic locomotion in the mit cheetah 3 through convex model-predictive control
Jared Di Carlo, Patrick M Wensing, Benjamin Katz, Gerardo Bledt, and Sangbae Kim · 2018
Earlier work this paper cites.
Per-contact iteration method for solving contact dynamics
Jemin Hwangbo, Joonho Lee, and Marco Hutter · 2018
Earlier work this paper cites.
Adaptive stress testing for autonomous vehicles
Mark Koren, Saud Alsaif, Ritchie Lee, and Mykel J Kochenderfer · 2018
Earlier work this paper cites.
The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery
Zachary C Lipton · 2018
Earlier work this paper cites.
Robust deep reinforcement learning with adversarial attacks
Anay Pattanaik, Zhenyi Tang, Shuijing Liu, Gautham Bommannan, and Girish Chowdhary · 2018
Earlier work this paper cites.
Multi-objective training of generative adversarial networks with multiple discriminators
Isabela Albuquerque, Joao Monteiro, Thang Doan, Breandan Considine, Tiago Falk, and Ioannis Mitliagkas · 2019
Earlier work this paper cites.
Adaptive stress testing with reward augmentation for autonomous vehicle validatio
Anthony Corso, Peter Du, Katherine Driggs-Campbell, and Mykel J Kochenderfer · 2019
Earlier work this paper cites.
Adversarial policies: Attacking deep reinforcement learning
Adam Gleave, Michael Dennis, Cody Wild, Neel Kant, Sergey Levine, and Stuart Russell · 2019
Earlier work this paper cites.
Learning agile and dynamic motor skills for legged robots
Jemin Hwangbo, Joonho Lee, Alexey Dosovitskiy, Dario Bellicoso, Vassilios Tsounis, Vladlen Koltun, and Marco Hutter · 2019
Earlier work this paper cites.
Neural lander: Stable drone landing control using learned dynamics
Guanya Shi, Xichen Shi, Michael O’Connell, Rose Yu, Kamyar Azizzadenesheli, Animashree Anandkumar, Yisong Yue, and Soon-Jo Chung · 2019
Earlier work this paper cites.
Theoretically principled trade-off between robustness and accuracy
Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric Xing, Laurent El Ghaoui, and Michael Jordan · 2019
Earlier work this paper cites.
Ad-vat+: An asymmetric dueling mechanism for learning and understanding visual active tracking
Fangwei Zhong, Peng Sun, Wenhan Luo, Tingyun Yan, and Yizhou Wang · 2019
Earlier work this paper cites.
Toward trustworthy ai development: mechanisms for supporting verifiable claims
Miles Brundage, Shahar Avin, Jasmine Wang, Haydn Belfield, Gretchen Krueger, Gillian Hadfield, Heidy Khlaaf, Jingying Yang, Helen Toner, Ruth Fong, et al · 2020
Cited alongside, same era.
Mpc-net: A first principles guided policy search
Jan Carius, Farbod Farshidian, and Marco Hutter · 2020
Cited alongside, same era.
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
Francesco Croce and Matthias Hein · 2020
Cited alongside, same era.
Learning quadrupedal locomotion over challenging terrain
Joonho Lee, Jemin Hwangbo, Lorenz Wellhausen, Vladlen Koltun, and Marco Hutter · 2020
Cited alongside, same era.
Learning agile robotic locomotion skills by imitating animals
Xue Bin Peng, Erwin Coumans, Tingnan Zhang, Tsang-Wei Lee, Jie Tan, and Sergey Levine · 2020
Cited alongside, same era.
Rapid locomotion via reinforcement learning
Gabriel B Margolis, Ge Yang, Kartik Paigwar, Tao Chen, and Pulkit Agrawal · 2022
Later among the works it cites.
Learning robust perceptive locomotion for quadrupedal robots in the wild
Takahiro Miki, Joonho Lee, Jemin Hwangbo, Lorenz Wellhausen, Vladlen Koltun, and Marco Hutter · 2022
Later among the works it cites.
Adversarial joint attacks on legged robots
Takuto Otomo, Hiroshi Kera, and Kazuhiko Kawamoto · 2022
Later among the works it cites.
Ase: Large-scale reusable adversarial skill embeddings for physically simulated characters
Xue Bin Peng, Yunrong Guo, Lina Halper, Sergey Levine, and Sanja Fidler · 2022
Later among the works it cites.
Learning to walk in minutes using massively parallel deep reinforcement learning
Nikita Rudin, David Hoeller, Philipp Reist, and Marco Hutter · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Learning agile locomotion via adversarial training
Yujin Tang, Jie Tan, and Tatsuya Harada · 2020
Cited alongside, same era.
Multi-expert learning of adaptive legged locomotion
Chuanyu Yang, Kai Yuan, Qiuguo Zhu, Wanming Yu, and Zhibin Li · 2020
Cited alongside, same era.
The diversified ensemble neural network
Shaofeng Zhang, Meng Liu, and Junchi Yan · 2020
Cited alongside, same era.
Robustbench: a standardized adversarial robustness benchmark
Francesco Croce, Maksym Andriushchenko, Vikash Sehwag, Edoardo Debenedetti, Nicolas Flammarion, Mung Chiang, Prateek Mittal, and Matthias Hein · 2021
Cited alongside, same era.
Enhancing continuous control of mobile robots for end-to-end visual active tracking
Alessandro Devo, Alberto Dionigi, and Gabriele Costante · 2021
Cited alongside, same era.
Rma: Rapid motor adaptation for legged robots
Ashish Kumar, Zipeng Fu, Deepak Pathak, and Jitendra Malik · 2021
Cited alongside, same era.
Reinforcement learning for robust parameterized locomotion control of bipedal robots
Zhongyu Li, Xuxin Cheng, Xue Bin Peng, Pieter Abbeel, Sergey Levine, Glen Berseth, and Koushil Sreenath · 2021
Cited alongside, same era.
Cerberus in the darpa subterranean challenge
Marco Tranzatto, Takahiro Miki, Mihir Dharmadhikari, Lukas Bernreiter, Mihir Kulkarni, Frank Mascarich, Olov Andersson, Shehryar Khattak, Marco Hutter, Roland Siegwart, et al · 2022
Later among the works it cites.
Quadruped capturability and push recovery via a switched-systems characterization of dynamic balance
Hua Chen, Zejun Hong, Shunpeng Yang, Patrick M Wensing, and Wei Zhang · 2023
Later among the works it cites.
Learning agile locomotion and adaptive behaviors via rl-augmented mpc
Yiyu Chen and Quan Nguyen · 2023
Later among the works it cites.
Extreme parkour with legged robots
Xuxin Cheng, Kexin Shi, Ananye Agarwal, and Deepak Pathak · 2023
Later among the works it cites.
Into the robotic depths: Analysis and insights from the darpa subterranean challenge
Timothy H Chung, Viktor Orekhov, and Angela Maio · 2023
Later among the works it cites.
Dense reinforcement learning for safety validation of autonomous vehicles
Shuo Feng, Haowei Sun, Xintao Yan, Haojie Zhu, Zhengxia Zou, Shengyin Shen, and Henry X Liu · 2023
Later among the works it cites.
Perceptive locomotion through nonlinear model-predictive control
Ruben Grandia, Fabian Jenelten, Shaohui Yang, Farbod Farshidian, and Marco Hutter · 2023
Later among the works it cites.
Grasping living objects with adversarial behaviors using inverse reinforcement learning
Zhe Hu, Yu Zheng, and Jia Pan · 2023
Later among the works it cites.
Champion-level drone racing using deep reinforcement learning
Elia Kaufmann, Leonard Bauersfeld, Antonio Loquercio, Matthias Müller, Vladlen Koltun, and Davide Scaramuzza · 2023
Later among the works it cites.
Versatile skill control via self-supervised adversarial imitation of unlabeled mixed motions
Chenhao Li, Sebastian Blaes, Pavel Kolev, Marin Vlastelica, Jonas Frey, and Georg Martius · 2023
Later among the works it cites.
Learning risk-aware quadrupedal locomotion using distributional reinforcement learning
Lukas Schneider, Jonas Frey, Takahiro Miki, and Marco Hutter · 2023
Later among the works it cites.
Robust quadrupedal locomotion via risk-averse policy learning
Jiyuan Shi, Chenjia Bai, Haoran He, Lei Han, Dong Wang, Bin Zhao, Xiu Li, and Xuelong Li · 2023
Later among the works it cites.
Advanced skills through multiple adversarial motion priors in reinforcement learning
Eric Vollenweider, Marko Bjelonic, Victor Klemm, Nikita Rudin, Joonho Lee, and Marco Hutter · 2023
Later among the works it cites.
Generating a terrain-robustness benchmark for legged locomotion: A prototype via terrain authoring and active learning
Chong Zhang and Lizhi Yang · 2023
Later among the works it cites.
Anymal parkour: Learning agile navigation for quadrupedal robots
David Hoeller, Nikita Rudin, Dhionis Sako, and Marco Hutter · 2024
Closest in time.
Learning locomotion for quadruped robots via distributional ensemble actor-critic
Sicen Li, Yiming Pang, Panju Bai, Jiawei Li, Zhaojin Liu, Shihao Hu, Liquan Wang, and Gang Wang · 2024
Closest in time.
Adversarial training should be cast as a non-zero-sum game
Alexander Robey, Fabian Latorre, George J. Pappas, Hamed Hassani, and Volkan Cevher · 2024
Closest in time.