Using deep reinforcement learning to learn high-level policies on the atrias biped
Learning controllers for bipedal robots is a challenging problem, often requiring expert
knowledge and extensive tuning of parameters that vary in different situations. Recently …
knowledge and extensive tuning of parameters that vary in different situations. Recently …
Reward-adaptive reinforcement learning: Dynamic policy gradient optimization for bipedal locomotion
Controlling a non-statically bipedal robot is challenging due to the complex dynamics and
multi-criterion optimization involved. Recent works have demonstrated the effectiveness of …
multi-criterion optimization involved. Recent works have demonstrated the effectiveness of …
Feedback control for cassie with deep reinforcement learning
Bipedal locomotion skills are challenging to develop. Control strategies often use local
linearization of the dynamics in conjunction with reduced-order abstractions to yield …
linearization of the dynamics in conjunction with reduced-order abstractions to yield …
Learning task space actions for bipedal locomotion
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 …
bipedal locomotion policies for real robots. This prior work, however, has focused on …
Learning memory-based control for human-scale bipedal locomotion
J Siekmann, S Valluri, J Dao, L Bermillo… - arXiv preprint arXiv …, 2020 - arxiv.org
Controlling a non-statically stable biped is a difficult problem largely due to the complex
hybrid dynamics involved. Recent work has demonstrated the effectiveness of reinforcement …
hybrid dynamics involved. Recent work has demonstrated the effectiveness of reinforcement …
Reinforcement learning for versatile, dynamic, and robust bipedal locomotion control
This paper presents a comprehensive study on using deep reinforcement learning (RL) to
create dynamic locomotion controllers for bipedal robots. Going beyond focusing on a single …
create dynamic locomotion controllers for bipedal robots. Going beyond focusing on a single …
Robust feedback motion policy design using reinforcement learning on a 3d digit bipedal robot
In this paper, a hierarchical and robust framework for learning bipedal locomotion is
presented and successfully implemented on the 3D biped robot Digit built by Agility …
presented and successfully implemented on the 3D biped robot Digit built by Agility …
Reinforcement learning for non-prehensile manipulation: Transfer from simulation to physical system
Reinforcement learning has emerged as a promising methodology for training robot
controllers. However, most results have been limited to simulation due to the need for a …
controllers. However, most results have been limited to simulation due to the need for a …
Torque-based deep reinforcement learning for task-and-robot agnostic learning on bipedal robots using sim-to-real transfer
In this letter, we review the question of which action space is best suited for controlling a real
biped robot in combination with Sim2Real training. Position control has been popular as it …
biped robot in combination with Sim2Real training. Position control has been popular as it …
Learning bipedal walking for humanoids with current feedback
Recent advances in deep reinforcement learning (RL) based techniques combined with
training in simulation have offered a new approach to developing robust controllers for …
training in simulation have offered a new approach to developing robust controllers for …