6 papers
Trajectory-Level Continuous Action Representation for Robotic Manipulation
Tong Yang, Jingkai Jia, Yuecheng Xu +3
We propose CAT, a trajectory-level continuous action representation framework for robotic manipulation. Existing visuomotor systems often entangle action representation with contro…
Representation-Aligned Tactile Grounding for Contact-Rich Robotic Manipulation
Ruilin Chen, Jingkai Jia, Tong Yang +8
Tactile-enhanced vision-language-action (VLA) policies have been introduced for contact-rich manipulation, where critical interaction states are often hidden from vision. Future ta…
EDAR: Learning Environment-Dependent Action Representations for Robotic Manipulation
Yuecheng Xu, Tong Yang, Jingkai Jia +3
Learning effective action representations is critical for robotic manipulation, where raw control trajectories are often noisy, redundant, and difficult to model directly. Existing…
VLA-Hijack: A Transferable Patch Attack against Vision-Language-Action Models via Visual Proprioception Hijacking
Jiyuan Fu, Kaixun Jiang, Jingkai Jia +7
While Vision-Language-Action (VLA) models have emerged as powerful generalist policies, their severe vulnerability to adversarial patches significantly hinders their deployment in…
LongBench: Evaluating Robotic Manipulation Policies on Real-World Long-Horizon Tasks
Xueyao Chen, Jingkai Jia, Tong Yang +3
Robotic manipulation policies often degrade over extended horizons, yet existing benchmarks provide limited insight into why such failures occur. Most prior benchmarks are either s…
Fast Visuomotor Policy for Robotic Manipulation
Jingkai Jia, Tong Yang, Xueyao Chen +2
We present a fast and effective policy framework for robotic manipulation, named Energy Policy, designed for high-frequency robotic tasks and resource-constrained systems. Unlike e…