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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/2506.23768 |
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| _version_ | 1866916065695498240 |
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| author | La Barbera, Vittorio Bohez, Steven Hasenclever, Leonard Tassa, Yuval Hutchinson, John R. |
| author_facet | La Barbera, Vittorio Bohez, Steven Hasenclever, Leonard Tassa, Yuval Hutchinson, John R. |
| contents | We introduce a novel musculoskeletal model of a dog, procedurally generated from accurate 3D muscle meshes. Accompanying this model is a motion capture-based locomotion task compatible with a variety of control algorithms, as well as an improved muscle dynamics model designed to enhance convergence in differentiable control frameworks. We validate our approach by comparing simulated muscle activation patterns with experimentally obtained electromyography (EMG) data from previous canine locomotion studies. This work aims to bridge gaps between biomechanics, robotics, and computational neuroscience, offering a robust platform for researchers investigating muscle actuation and neuromuscular control.We plan to release the full model along with the retargeted motion capture clips to facilitate further research and development. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_23768 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Motion Tracking with Muscles: Predictive Control of a Parametric Musculoskeletal Canine Model La Barbera, Vittorio Bohez, Steven Hasenclever, Leonard Tassa, Yuval Hutchinson, John R. Robotics We introduce a novel musculoskeletal model of a dog, procedurally generated from accurate 3D muscle meshes. Accompanying this model is a motion capture-based locomotion task compatible with a variety of control algorithms, as well as an improved muscle dynamics model designed to enhance convergence in differentiable control frameworks. We validate our approach by comparing simulated muscle activation patterns with experimentally obtained electromyography (EMG) data from previous canine locomotion studies. This work aims to bridge gaps between biomechanics, robotics, and computational neuroscience, offering a robust platform for researchers investigating muscle actuation and neuromuscular control.We plan to release the full model along with the retargeted motion capture clips to facilitate further research and development. |
| title | Motion Tracking with Muscles: Predictive Control of a Parametric Musculoskeletal Canine Model |
| topic | Robotics |
| url | https://arxiv.org/abs/2506.23768 |