Robust Quadrupedal Locomotion Via Risk Averse Policy Learning

Robust Quadrupedal Locomotion Via Risk-Averse Policy Learning | DeepAI

Robust Quadrupedal Locomotion Via Risk-Averse Policy Learning | DeepAI In this paper, we consider a novel risk sensitive perspective to enhance the robustness of legged locomotion. This work proposes a novel quadrupedal locomotion learning framework that allows quadru pedal robots to walk through challenging terrains, even with limited sensing modalities, and was validated in real world outdoor environments with varying conditions within a single run for a long distance.

Robust Quadrupedal Locomotion Via Risk-Averse Policy Learning

Robust Quadrupedal Locomotion Via Risk-Averse Policy Learning Robust quadrupedal locomotion via risk averse policy learning. in ieee international conference on robotics and automation, icra 2024, yokohama, japan, may 13 17, 2024. pages 11459 11466, ieee, 2024. [doi]. Pytorch implementation of the risk averse ppo (ra ppo) from the paper robust quadrupedal locomotion via risk averse policy learning currently only a discrete action space implementation!. We present a novel perspective on achieving a robust lo comotion controller via distributional value function and risk sensitive policy learning. the resulted controller enabled the robot to resist heavy impact and traverse challenging terrains. Extensive experiments in both simulation environments and a real aliengo robot demonstrate that our method is efficient in handling various external disturbances, and the resulting policy exhibits improved robustness in harsh and uncertain situations in legged locomotion.

Robust Quadrupedal Locomotion Via Risk-Averse Policy Learning

Robust Quadrupedal Locomotion Via Risk-Averse Policy Learning We present a novel perspective on achieving a robust lo comotion controller via distributional value function and risk sensitive policy learning. the resulted controller enabled the robot to resist heavy impact and traverse challenging terrains. Extensive experiments in both simulation environments and a real aliengo robot demonstrate that our method is efficient in handling various external disturbances, and the resulting policy exhibits improved robustness in harsh and uncertain situations in legged locomotion. Robust quadrupedal locomotion via risk averse policy learning: paper and code. the robustness of legged locomotion is crucial for quadrupedal robots in challenging terrains. This paper investigates how vulnerabilities of nns affect the worst case safety of quadrupedal locomotion controllers, and how they can be "patched" to robustify the controllers. Despite its importance, these risks are not explicitly modeled by currently deployed locomotion controllers for legged robots. in this work, we propose a risk sensitive locomotion training method employing distributional reinforcement learning to consider safety explicitly. Abstract:we present zsl rppo, an improved zero shot learning architecture that overcomes the limitations of teacher student neural networks and enables generating robust, reliable, and versatile locomotion for quadrupedal robots in challenging terrains.

Robust Quadrupedal Locomotion Via Risk-Averse Policy Learning (Link In Comments) : R/singularity

Robust Quadrupedal Locomotion Via Risk-Averse Policy Learning (Link In Comments) : R/singularity Robust quadrupedal locomotion via risk averse policy learning: paper and code. the robustness of legged locomotion is crucial for quadrupedal robots in challenging terrains. This paper investigates how vulnerabilities of nns affect the worst case safety of quadrupedal locomotion controllers, and how they can be "patched" to robustify the controllers. Despite its importance, these risks are not explicitly modeled by currently deployed locomotion controllers for legged robots. in this work, we propose a risk sensitive locomotion training method employing distributional reinforcement learning to consider safety explicitly. Abstract:we present zsl rppo, an improved zero shot learning architecture that overcomes the limitations of teacher student neural networks and enables generating robust, reliable, and versatile locomotion for quadrupedal robots in challenging terrains.

Risk-averse-locomotion.github.io/index.html At Main · Risk-averse-locomotion/risk-averse ...

Risk-averse-locomotion.github.io/index.html At Main · Risk-averse-locomotion/risk-averse ... Despite its importance, these risks are not explicitly modeled by currently deployed locomotion controllers for legged robots. in this work, we propose a risk sensitive locomotion training method employing distributional reinforcement learning to consider safety explicitly. Abstract:we present zsl rppo, an improved zero shot learning architecture that overcomes the limitations of teacher student neural networks and enables generating robust, reliable, and versatile locomotion for quadrupedal robots in challenging terrains.

Robust Quadrupedal Locomotion via Risk-Averse Policy Learning