Zobrazeno 1 - 10
of 148
pro vyhledávání: '"Hurst, Jonathan"'
For legged robots to match the athletic capabilities of humans and animals, they must not only produce robust periodic walking and running, but also seamlessly switch between nominal locomotion gaits and more specialized transient maneuvers. Despite
Externí odkaz:
http://arxiv.org/abs/2207.07835
Autor:
Batke, Ryan, Yu, Fangzhou, Dao, Jeremy, Hurst, Jonathan, Hatton, Ross L., Fern, Alan, Green, Kevin
In this work, we propose a method to generate reduced-order model reference trajectories for general classes of highly dynamic maneuvers for bipedal robots for use in sim-to-real reinforcement learning. Our approach is to utilize a single rigid-body
Externí odkaz:
http://arxiv.org/abs/2207.04163
In this work, we propose a learning approach for 3D dynamic bipedal walking when footsteps are constrained to stepping stones. While recent work has shown progress on this problem, real-world demonstrations have been limited to relatively simple open
Externí odkaz:
http://arxiv.org/abs/2205.01807
Recent work on sim-to-real learning for bipedal locomotion has demonstrated new levels of robustness and agility over a variety of terrains. However, that work, and most prior bipedal locomotion work, have not considered locomotion under a variety of
Externí odkaz:
http://arxiv.org/abs/2204.04340
The complex dynamics of agile robotic legged locomotion requires motion planning to intelligently adjust footstep locations. Often, bipedal footstep and motion planning use mathematically simple models such as the linear inverted pendulum, instead of
Externí odkaz:
http://arxiv.org/abs/2203.15107
Autor:
Duan, Helei, Malik, Ashish, Dao, Jeremy, Saxena, Aseem, Green, Kevin, Siekmann, Jonah, Fern, Alan, Hurst, Jonathan
Recently, work on reinforcement learning (RL) for bipedal robots has successfully learned controllers for a variety of dynamic gaits with robust sim-to-real demonstrations. In order to maintain balance, the learned controllers have full freedom of wh
Externí odkaz:
http://arxiv.org/abs/2203.07589
In this paper, we investigate whether applying ankle torques during mid-stance can be a more effective way to reduce energetic cost of locomotion than actuating leg length alone. Ankles are useful in human gaits for many reasons including static bala
Externí odkaz:
http://arxiv.org/abs/2111.14987
Accurate and precise terrain estimation is a difficult problem for robot locomotion in real-world environments. Thus, it is useful to have systems that do not depend on accurate estimation to the point of fragility. In this paper, we explore the limi
Externí odkaz:
http://arxiv.org/abs/2105.08328
Recent work has demonstrated the success of reinforcement learning (RL) for training bipedal locomotion policies for real robots. This prior work, however, has focused on learning joint-coordination controllers based on an objective of following join
Externí odkaz:
http://arxiv.org/abs/2011.04741
We study the problem of realizing the full spectrum of bipedal locomotion on a real robot with sim-to-real reinforcement learning (RL). A key challenge of learning legged locomotion is describing different gaits, via reward functions, in a way that i
Externí odkaz:
http://arxiv.org/abs/2011.01387