{"ID":2870016,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.14353","arxiv_id":"2509.14353","title":"DreamControl: Human-Inspired Whole-Body Humanoid Control for Scene Interaction via Guided Diffusion","abstract":"We introduce DreamControl, a novel methodology for learning autonomous whole-body humanoid skills. DreamControl leverages the strengths of diffusion models and Reinforcement Learning (RL): our core innovation is the use of a diffusion prior trained on human motion data, which subsequently guides an RL policy in simulation to complete specific tasks of interest (e.g., opening a drawer or picking up an object). We demonstrate that this human motion-informed prior allows RL to discover solutions unattainable by direct RL, and that diffusion models inherently promote natural looking motions, aiding in sim-to-real transfer. We validate DreamControl's effectiveness on a Unitree G1 robot across a diverse set of challenging tasks involving simultaneous lower and upper body control and object interaction. Project website at https://genrobo.github.io/DreamControl/","short_abstract":"We introduce DreamControl, a novel methodology for learning autonomous whole-body humanoid skills. DreamControl leverages the strengths of diffusion models and Reinforcement Learning (RL): our core innovation is the use of a diffusion prior trained on human motion data, which subsequently guides an RL policy in simulat...","url_abs":"https://arxiv.org/abs/2509.14353","url_pdf":"https://arxiv.org/pdf/2509.14353v3","authors":"[\"Dvij Kalaria\",\"Sudarshan S Harithas\",\"Pushkal Katara\",\"Sangkyung Kwak\",\"Sarthak Bhagat\",\"Shankar Sastry\",\"Srinath Sridhar\",\"Sai Vemprala\",\"Ashish Kapoor\",\"Jonathan Chung-Kuan Huang\"]","published":"2025-09-17T18:35:43Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.AI\",\"cs.LG\"]","methods":"[\"Reinforcement Learning\",\"Diffusion Model\"]","has_code":false}