8 citations · 8 across the 5 of their papers we have counts for
6 papers
TAPNAV: Humanoid Navigation through Tactile Active Perception
Huaze Liu, Zhenyu Wu, Jaehwi Jang +8
Navigation in vision-denied environments is challenging for humanoid robots because proprioceptive odometry drifts and localization uncertainty accumulates rapidly. We present TAPN…
Opt2VLA: Force-Aware Vision-Language-Action for Contact-Rich Humanoid Whole-Body Manipulation
Fukang Liu, Yipu Chen, Jaehwi Jang +3
Humanoid robots are expected to perform diverse human-level tasks in daily environments, many of which require precise regulation of interaction forces. While recent vision-languag…
WT-UMI: Tactile-based Whole-Body Manipulation via Force-Supervised Contact-Aware Planning
Jaehwi Jang, Zhaoyuan Gu, Alfred Cueva +15
Whole-body humanoid manipulation of bulky, deformable, and shared-load objects requires distributed contact sensing and explicit force regulation, yet most imitation policies treat…
SEEC: Stable End-Effector Control with Model-Enhanced Residual Learning for Humanoid Loco-Manipulation
Jaehwi Jang, Zhuoheng Wang, Ziyi Zhou +2
Arm end-effector stabilization is essential for humanoid loco-manipulation tasks, yet it remains challenging due to the high degrees of freedom and inherent dynamic instability of…
Opt2Skill: Imitating Dynamically-feasible Whole-Body Trajectories for Versatile Humanoid Loco-Manipulation
Fukang Liu, Zhaoyuan Gu, Yilin Cai +8
Humanoid robots are designed to perform diverse loco-manipulation tasks. However, they face challenges due to their high-dimensional and unstable dynamics, as well as the complex c…
Learn to Teach: Sample-Efficient Privileged Learning for Humanoid Locomotion over Diverse Terrains
Feiyang Wu, Xavier Nal, Jaehwi Jang +4
Humanoid robots promise transformative capabilities for industrial and service applications. While recent advances in Reinforcement Learning (RL) yield impressive results in locomo…