6 papers · 1 filter
DiagGen: Agentic Generation of Deformable Assets with Sim-based Diagnostics for Robotic Simulation
Guanxiong Chen, Yiduo Qu, Qianjun Xia +12
While simulation-ready deformable assets are essential for in-silico robotic manipulation tasks, existing generation frameworks typically assess physical plausibility after generat…
Agentic Real2Sim: Physics-based World Modeling with Vision-Language Agents
Guanxiong Chen, Qianjun Xia, Jiawei Peng +24
Real-to-sim conversion for robotic interaction with objects remains labor-intensive because it requires more than visual reconstruction: a streamlined real2sim process must recover…
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…
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-…
FLASH: Fast Learning via GPU-Accelerated Simulation for High-Fidelity Deformable Manipulation in Minutes
Siyuan Luo, Bingyang Zhou, Chong Zhang +9
Simulation frameworks such as Isaac Sim have enabled scalable robot learning for locomotion and rigid-body manipulation; however, contact-rich simulation remains a major bottleneck…
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…