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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.16943 |
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| _version_ | 1866908867595599872 |
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| author | Zhang, Haozhuo Sun, Jingkai Caprio, Michele Tang, Jian Zhang, Shanghang Zhang, Qiang Pan, Wei |
| author_facet | Zhang, Haozhuo Sun, Jingkai Caprio, Michele Tang, Jian Zhang, Shanghang Zhang, Qiang Pan, Wei |
| contents | We introduce LHM-Humanoid, a benchmark and learning framework for long-horizon whole-body humanoid loco-manipulation in diverse, cluttered scenes. In our setting, multiple objects are displaced from their intended locations and may obstruct navigation; a humanoid agent must repeatedly (i) walk to a target, (ii) pick it up with diverse whole-body postures under balance constraints, (iii) carry it while navigating around obstacles, and (iv) place it at a designated goal -- all within a single continuous episode and without any environment reset. This task simultaneously demands cross-scene generalization and unified one-policy control: layouts, obstacle arrangements, object category/mass/shape/color and object start/goal poses vary substantially even within a room category, requiring a single general policy that directly outputs actions rather than invoking pre-trained skill libraries. Our dataset spans four room types (bedroom, living room, kitchen, and warehouse), comprising 350 diverse scenes/tasks with 79 objects (25 movable targets). Since no scene-specific ground-truth motion sequences are provided, we learn goal-conditioned teacher policies via reinforcement learning and distill them into a single end-to-end student policy using DAgger. We further distill this unified policy into a vision-language-action (VLA) model driven by egocentric RGB observations and natural language. Experiments in Isaac Gym demonstrate that LHM-Humanoid substantially outperforms end-to-end RL baselines and prior humanoid loco-manipulation methods on both seen and unseen scenes, exhibiting strong long-horizon robustness and cross-scene generalization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_16943 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LHM-Humanoid: Learning a Unified Policy for Long-Horizon Humanoid Whole-Body Loco-Manipulation in Diverse Messy Environments Zhang, Haozhuo Sun, Jingkai Caprio, Michele Tang, Jian Zhang, Shanghang Zhang, Qiang Pan, Wei Robotics Artificial Intelligence We introduce LHM-Humanoid, a benchmark and learning framework for long-horizon whole-body humanoid loco-manipulation in diverse, cluttered scenes. In our setting, multiple objects are displaced from their intended locations and may obstruct navigation; a humanoid agent must repeatedly (i) walk to a target, (ii) pick it up with diverse whole-body postures under balance constraints, (iii) carry it while navigating around obstacles, and (iv) place it at a designated goal -- all within a single continuous episode and without any environment reset. This task simultaneously demands cross-scene generalization and unified one-policy control: layouts, obstacle arrangements, object category/mass/shape/color and object start/goal poses vary substantially even within a room category, requiring a single general policy that directly outputs actions rather than invoking pre-trained skill libraries. Our dataset spans four room types (bedroom, living room, kitchen, and warehouse), comprising 350 diverse scenes/tasks with 79 objects (25 movable targets). Since no scene-specific ground-truth motion sequences are provided, we learn goal-conditioned teacher policies via reinforcement learning and distill them into a single end-to-end student policy using DAgger. We further distill this unified policy into a vision-language-action (VLA) model driven by egocentric RGB observations and natural language. Experiments in Isaac Gym demonstrate that LHM-Humanoid substantially outperforms end-to-end RL baselines and prior humanoid loco-manipulation methods on both seen and unseen scenes, exhibiting strong long-horizon robustness and cross-scene generalization. |
| title | LHM-Humanoid: Learning a Unified Policy for Long-Horizon Humanoid Whole-Body Loco-Manipulation in Diverse Messy Environments |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2508.16943 |