7 papers
Safe-Night VLA: Seeing the Unseen via Thermal-Perceptive Vision-Language-Action Models for Safety-Critical Manipulation
Dian Yu, Qingchuan Zhou, Bingkun Huang +2
The paper introduces Safe-Night VLA, a robot manipulation system that combines long-wave infrared thermal sensing with a vision‑language backbone and adds safety guarantees via con…
Safe Consensus of Cooperative Manipulation with Hierarchical Event-Triggered Control Barrier Functions
Simiao Zhuang, Bingkun Huang, Zewen Yang
Cooperative transport and manipulation of heavy or bulky payloads by multiple manipulators requires coordinated formation tracking, while simultaneously enforcing strict safety con…
Contact-Safe Reinforcement Learning with ProMP Reparameterization and Energy Awareness
Bingkun Huang, Yuhe Gong, Zewen Yang +2
Reinforcement learning (RL) approaches based on Markov Decision Processes (MDPs) are predominantly applied in the robot joint space, often relying on limited task-specific informat…
A Unified Complementarity-based Approach for Rigid-Body Manipulation and Motion Prediction
Bingkun Huang, Xin Ma, Nilanjan Chakraborty +1
Robotic manipulation in unstructured environments requires planners to reason jointly about free-space motion and sustained, frictional contact with the environment. Existing (loca…
Streaming Generated Gaussian Process Experts for Online Learning and Control: Extended Version
Zewen Yang, Dongfa Zhang, Xiaobing Dai +5
Gaussian Processes (GPs), as a nonparametric learning method, offer flexible modeling capabilities and calibrated uncertainty quantification for function approximations. Additional…
Prompt2Auto: From Motion Prompt to Automated Control via Geometry-Invariant One-Shot Gaussian Process Learning
Zewen Yang, Xiaobing Dai, Dongfa Zhang +5
Learning from demonstration allows robots to acquire complex skills from human demonstrations, but conventional approaches often require large datasets and fail to generalize acros…