Ego-Vision World Model for Humanoid Contact Planning

ArXi:2510.11682v2 Announce Type: replace-cross Enabling humanoid robots to exploit physical contact, rather than simply avoid collisions, is crucial for autonomy in unstructured environments. Traditional optimization-based planners struggle with contact complexity, while on-policy reinforcement learning (RL) is sample-inefficient and has limited multi-task ability. We propose a framework combining a learned world model with sampling-based Model Predictive Control (MPC), trained on a nstration-free offline dataset to predict future outcomes in a compressed latent space.