3 papers
cs.RO2026
PhysCaP: Grounding Code-as-Policy Agent with Physics-Informed Exploration
Chen-Yu Lin, Jing-Wen Chen, Hsueh-En Chang +8
We present PhysCaP, a Physics-Informed Code-as-Policy agent system for active perception in robotic manipulation. While vision-language-action policies excel at imitating demonstra…
cs.AI2025
Learning Skills from Action-Free Videos
Hung-Chieh Fang, Kuo-Han Hung, Chu-Rong Chen +7
Learning from videos offers a promising path toward generalist robots by providing rich visual and temporal priors beyond what real robot datasets contain. While existing video gen…
cs.RO2025
Video Replanning via Latent Embedding Refinement and Rejection-Based Sampling
Po-Chen Ko, Yu Chan, Jiayuan Mao +7
Video planning has emerged as a flexible framework for robot manipulation, in which a generative model predicts a video of task completion, and a downstream module translates the p…