Year: 2026
Type: conference-paper
Abstract: Controlling robot astronauts in microgravity is challenging due to complex floating-base dynamics and the scarcity of motion-capture data. This paper proposes a video-driven pipeline to acquire free-floating mobility skills directly from public astronaut footage, serving as a foundational step toward complex biomechanical control in space. Monocular motion reconstruction and kinematic retargeting are employed to generate physically feasible robot references. These priors guide a Goal-Conditioned Reinforcement Learning (GCRL) framework augmented with Adversarial Motion Priors (AMP) to ensure precise tracking and human-like style. Simulations validate the pipeline, demonstrating the feasibility of utilizing monocular video as a supervision source for space robotics.
Authors: Liping Fang, Jun Zhang, Liang Tang
Institution: Beijing Institute of Control Engineering
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7162027474