{"ID":2856504,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.11682","arxiv_id":"2510.11682","title":"Ego-Vision World Model for Humanoid Contact Planning","abstract":"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 demonstration-free offline dataset to predict future outcomes in a compressed latent space. To address sparse contact rewards and sensor noise, the MPC uses a learned surrogate value function for dense, robust planning. Our single, scalable model supports contact-aware tasks, including wall support after perturbation, blocking incoming objects, and traversing height-limited arches, with improved sample efficiency and multi-task capability over on-policy RL. Deployed on a physical humanoid, our system achieves robust, real-time contact planning from proprioception and ego-centric depth images. Code and dataset are available at our website: https://ego-vcp.github.io/","short_abstract":"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....","url_abs":"https://arxiv.org/abs/2510.11682","url_pdf":"https://arxiv.org/pdf/2510.11682v2","authors":"[\"Hang Liu\",\"Yuman Gao\",\"Sangli Teng\",\"Yufeng Chi\",\"Yakun Sophia Shao\",\"Zhongyu Li\",\"Maani Ghaffari\",\"Koushil Sreenath\"]","published":"2025-10-13T17:47:39Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.AI\",\"eess.SY\"]","methods":"[\"Reinforcement Learning\"]","has_code":false}