3 papers
cs.RO2026
EgoCPT: Learning Dexterous Manipulation from a Single Ego-Centric Video with Contact-Aware Optimization
Zhixin Sun, Kairui Shi, Tianao Ren +3
While expert demonstrations on real robots are difficult to collect, human ego-centric videos are easy to collect and abundant on the internet. However, learning dexterous manipula…
cs.RO2026
HapticWorld: an Interactive World Simulator with Real-time Torque Feedback
Shaoting Peng, Litian Liang, Yixuan Wang +4
Contact-rich manipulation depends on force sensing that is hard to infer from visual signals alone, both for collecting demonstrations and for training policies. Force-annotated da…
cs.RO2021
Learning to Fly -- a Gym Environment with PyBullet Physics for Reinforcement Learning of Multi-agent Quadcopter Control
Jacopo Panerati, Hehui Zheng, SiQi Zhou +3
Robotic simulators are crucial for academic research and education as well as the development of safety-critical applications. Reinforcement learning environments -- simple simulat…