4 accepted papers
A significant bottleneck in humanoid policy learning is the acquisition of large-scale, diverse datasets, as collecting reliable real-world data remains both difficult and cost-prohibitive. To address this limitation, we introduce HumanoidExo, a novel system that transfers human motion to whole-body…
The ability of autonomous subtask generation is important for robots to effectively cope with unforeseen situations during indoor search and rescue missions. While prior work mainly focused on improving individual low-level skills of the rescue robot, this paper proposes AutoExpand: a high-level fra
Articulated tracked robots face significant challenges in maintaining stable locomotion over uneven terrain due to unknown contact points between tracks and ground, which are critical for dynamic control. Unlike legged robots, where contact locations can be predicted, tracked systems require real-ti
The evolution from motion capture and teleoperation to robot skill learning has emerged as a hotspot and critical pathway for advancing embodied intelligence. However, existing systems still face a persistent gap in simultaneously achieving four objectives: accurate tracking of full upper limb movem