IMPACT: Learning Internal-Model Predictive Control for Forceful Robotic Manipulation 事件

IMPACT: Learning Internal-Model Predictive Control for Forceful Robotic Manipulation arXiv:2606.10818v1 Announce Type: cross Abstract: Real-world robotic manipulation tasks often involve forceful interactions with the environment, such as using tools of varying weights, transporting objects with different masses, and performing contact-rich tasks like table wiping. Previous learning-based approaches typically employ imitation learning policies that output target end-effector poses tracked by lo