5 citations · 9 across the 13 of their papers we have counts for
5 papers · 1 filter
Skill2Real: Agentic Skill Learning for Zero-Shot Sim-to-Real Robot Manipulation
Xincheng He, Siyu Ma, Chang Yu +5
Transferring robotic skills from simulation to reality requires task knowledge that remains usable across differences in perception, dynamics, and embodiment. We introduce Skill2Re…
FetchMan: Learning Visual Humanoid Loco-Manipulation Policies from Simulated Experiences
Omar Rayyan, Zhi Li, Max Argus +4
Visual loco-manipulation policies that can generalize to novel scenes and objects have long been a goal of robotics research. However, today's data-hungry algorithms make collectin…
MemoryVAM: Integrating Memory into Video Action Model for Robot Manipulation
Yuxin Jiang, Chang Yu, Yunuo Chen +4
Video-world-model policies learn action-relevant representations by predicting future observations. However, they condition on only a short observation window, which renders long-h…
Sparse2Act: Learning Action-Aligned Sparse 3D Representations for Cross-Domain Robot Manipulation
Yu Guo, Chang Yu, Siyu Ma +4
Explicit 3D representations are attractive for manipulation because they expose object shape, workspace geometry, and robot-object relations in metric coordinates. However, sparse…
TacCoRL: Integrating Tactile Feedback into VLA via Simulation
Siyu Ma, Yuqi Liang, Chang Yu +5
Vision-language-action (VLA) models provide strong visual, language, and action priors for robot manipulation, but visual observations alone often miss the local contact state requ…