8 papers
RARM: Confidence-Gated Progress Reward Modeling for RL in Manipulation
Pengzhi Yang, Xinyu Wang, Pengyu Jing +7
Reinforcement learning for robot manipulation is often bottlenecked by reward design, especially in long-horizon tasks: sparse success rewards provide weak supervision, while hand-…
Enabling Robust Cloth Manipulation via Inference-Time Simulator-in-the-Loop Refinement
Xin Liu, Yulin Li, Ziming Li +7
Simulator-in-the-loop optimization offers a promising inference-time mechanism for robot manipulation. It uses a physical simulator as a backend rollout engine to evaluate candidat…
Iterative Convex Optimization with Control Barrier Functions for Obstacle Avoidance among Polytopes
Shuo Liu, Zhe Huang, Calin A. Belta
Obstacle avoidance of polytopic obstacles by polytopic robots is a challenging problem in optimization-based control and trajectory planning. Many existing methods rely on smooth g…
Reasoning as Intersection: Consensus-Frame Alignment for Visual Focus in Video-MLLMs
Chengwen Liu, Zhe Huang, Jisheng Dang +3
Reinforcement learning has improved the reasoning ability of large language models, but applying outcome-only rewards to video multimodal large language models (Video-MLLMs) provid…
Few-Shot Neural Differentiable Simulator: Real-to-Sim Rigid-Contact Modeling
Zhenhao Huang, Siyuan Luo, Bingyang Zhou +3
Accurate physics simulation is essential for robotic learning and control, yet analytical simulators often fail to capture complex contact dynamics, while learning-based simulators…
Hierarchical Intention Tracking with Switching Trees for Real-Time Adaptation to Dynamic Human Intentions during Collaboration
Zhe Huang, Ye-Ji Mun, Fatemeh Cheraghi Pouria +1
During collaborative tasks, human behavior is guided by multiple levels of intentions that evolve over time, such as task sequence preferences and interaction strategies. To adapt…