9 papers
Agentic Real2Sim: Physics-based World Modeling with Vision-Language Agents
Guanxiong Chen, Qianjun Xia, Jiawei Peng +21
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…
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…
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…
SCRWKV: Ultra-Compact Structure-Calibrated Vision-RWKV for Topological Crack Segmentation
Hanxu Zhang, Chen Jia, Hui Liu +3
Achieving pixel-level accurate segmentation of structural cracks across diverse scenarios remains a formidable challenge. Existing methods face significant bottlenecks in balancing…
Fast and Reliable Gradients for Deformables Across Frictional Contact Regimes
Ziqiu Zeng, Gang Yang, Zhenhao Huang +5
Differentiable simulation establishes the mathematical foundation for solving challenging inverse problems in computer graphics and robotics, such as physical system identification…