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TERRA：面向肌肉骨骼运动的 terrain-aware 重建、重定向与控制
AI 导读
TERRA 是一套端到端流水线，仅凭运动学轨迹即可结合地形先验、估计接触与负自由空间证据，恢复任务相关的支撑几何，并在重定向中考虑解剖、肌腱连续性与接触约束。基于五个数据集生成的运动-地形配对，团队用 9.4 小时多样化运动数据训练出单一肌肉驱动控制策略，在重建、重定向与留出跟踪基准上提升地形精度、大幅减少解剖与交互违规，并在各类支撑地形上取得最高完成率。
正文
Abstract:Recent advances in musculoskeletal modeling and reinforcement learning have enabled muscle-actuated agents to reproduce increasingly complex human motions. Yet these capabilities remain largely confined to flat ground, in part because motion datasets rarely include aligned terrain geometry and because retargeting terrain interactions to complex musculoskeletal bodies is challenging. We present TERRA, an end-to-end pipeline for terrain-aware retargeting and control of musculoskeletal locomotion. From kinematic trajectories alone, TERRA combines terrain priors, estimated contacts, and negative free-space evidence to recover task-relevant support geometry. TERRA further considers anatomical, tendon-continuity, and contact constraints during retargeting. Using the resulting motion-terrain pairs from five datasets, we successfully train a single muscle-actuated control policy on 9.4 hours of diverse locomotion. Across reconstruction, retargeting, and held-out tracking benchmarks, TERRA improves terrain accuracy, sharply reduces anatomical and interaction violations, and achieves the highest observed completion rate over supported terrain families. Overall, TERRA provides a practical route from scene-less motion data to muscle-actuated locomotion over diverse non-flat terrain. Project website: this https URL
| Subjects: | Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Neurons and Cognition (q-bio.NC) |
| Cite as: | arXiv:2609.38653 [cs.RO] |
| (or arXiv:2609.38653v1 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2609.38653 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Alexander Mathis [view email]
[v1]
Tue, 29 Sep 2026 23:19:24 UTC (12,583 KB)
来源：HuggingFace Daily Papers（社区热门论文） · arxiv.org