Zobrazeno 1 - 10
of 71
pro vyhledávání: '"Hereid, Ayonga"'
Safe navigation in real-time is an essential task for humanoid robots in real-world deployment. Since humanoid robots are inherently underactuated thanks to unilateral ground contacts, a path is considered safe if it is obstacle-free and respects the
Externí odkaz:
http://arxiv.org/abs/2411.03619
Locomotion on dynamic rigid surface (i.e., rigid surface accelerating in an inertial frame) presents complex challenges for controller design, which are essential for deploying humanoid robots in dynamic real-world environments such as moving trains,
Externí odkaz:
http://arxiv.org/abs/2409.08371
Autor:
Paredes, Victor C., Hagen, Daniel A., Chesebrough, Samuel W., Swann, Riley, Garagic, Denis, Hereid, Ayonga
The Angular-Momentum Linear Inverted Pendulum (ALIP) model is a promising motion planner for bipedal robots. However, it relies on two assumptions: (1) the robot has point-contact feet or passive ankles, and (2) the angular momentum around the center
Externí odkaz:
http://arxiv.org/abs/2408.05308
Safe path and gait planning are essential for bipedal robots to navigate complex real-world environments. The prevailing approaches often plan the path and gait separately in a hierarchical fashion, potentially resulting in unsafe movements due to ne
Externí odkaz:
http://arxiv.org/abs/2403.17347
Traditional one-step preview planning algorithms for bipedal locomotion struggle to generate viable gaits when walking across terrains with restricted footholds, such as stepping stones. To overcome such limitations, this paper introduces a novel mul
Externí odkaz:
http://arxiv.org/abs/2403.17136
Autor:
Paredes, Victor, Hereid, Ayonga
Complex robotic systems require whole-body controllers to deal with contact interactions, handle closed kinematic chains, and track task-space control objectives. However, for many applications, safety-critical controllers are important to steer away
Externí odkaz:
http://arxiv.org/abs/2311.08409
This work presents a hierarchical framework for bipedal locomotion that combines a Reinforcement Learning (RL)-based high-level (HL) planner policy for the online generation of task space commands with a model-based low-level (LL) controller to track
Externí odkaz:
http://arxiv.org/abs/2309.15442
This paper presents a novel framework for learning robust bipedal walking by combining a data-driven state representation with a Reinforcement Learning (RL) based locomotion policy. The framework utilizes an autoencoder to learn a low-dimensional lat
Externí odkaz:
http://arxiv.org/abs/2309.15740
Dynamic locomotion in legged robots is close to industrial collaboration, but a lack of standardized testing obstructs commercialization. The issues are not merely political, theoretical, or algorithmic but also physical, indicating limited studies a
Externí odkaz:
http://arxiv.org/abs/2308.14636
Controller design for bipedal walking on dynamic rigid surfaces (DRSes), which are rigid surfaces moving in the inertial frame (e.g., ships and airplanes), remains largely uninvestigated. This paper introduces a hierarchical control approach that ach
Externí odkaz:
http://arxiv.org/abs/2210.13371