DreamControl: Human-Inspired Whole-Body Humanoid Control for Scene Interaction via Guided Diffusion
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arXiv
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| Format: | Preprint |
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2025
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| author | Kalaria, Dvij Harithas, Sudarshan S Katara, Pushkal Kwak, Sangkyung Bhagat, Sarthak Sastry, Shankar Sridhar, Srinath Vemprala, Sai Kapoor, Ashish Huang, Jonathan Chung-Kuan |
| author_facet | Kalaria, Dvij Harithas, Sudarshan S Katara, Pushkal Kwak, Sangkyung Bhagat, Sarthak Sastry, Shankar Sridhar, Srinath Vemprala, Sai Kapoor, Ashish Huang, Jonathan Chung-Kuan |
| contents | 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/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_14353 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DreamControl: Human-Inspired Whole-Body Humanoid Control for Scene Interaction via Guided Diffusion Kalaria, Dvij Harithas, Sudarshan S Katara, Pushkal Kwak, Sangkyung Bhagat, Sarthak Sastry, Shankar Sridhar, Srinath Vemprala, Sai Kapoor, Ashish Huang, Jonathan Chung-Kuan Robotics Artificial Intelligence Machine Learning 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/ |
| title | DreamControl: Human-Inspired Whole-Body Humanoid Control for Scene Interaction via Guided Diffusion |
| topic | Robotics Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2509.14353 |