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Assistance Torque Estimation via Dynamics-Aware Optimization for Lower-Limb Exoskeleton in Complex Environments — arXiv:2609.15352

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.15352
License (as archived): CC0-1.0
Archived: 2026-10-16T02:14:47.728Z
SHA-256 of the archived text: af68d956bd282fe0d0866b80d5871d6cead0bf5596fe537a2eaa395a19f43e90
Stable URL of this page: https://metraipolis.com/documento/dc_6ea26ddef2d7

How to cite

Assistance Torque Estimation via Dynamics-Aware Optimization for Lower-Limb Exoskeleton in Complex Environments — arXiv:2609.15352 — arXiv (en), https://arxiv.org/abs/2609.15352 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_6ea26ddef2d7 (Metraipolis archive dc_6ea26ddef2d7, sha256 af68d956bd282fe0d0866b80d5871d6cead0bf5596fe537a2eaa395a19f43e90)

BibTeX

@misc{dc6ea26ddef2d7,
  title = {Assistance Torque Estimation via Dynamics-Aware Optimization for Lower-Limb Exoskeleton in Complex Environments — arXiv:2609.15352},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.15352},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_6ea26ddef2d7; sha256 af68d956bd282fe0d0866b80d5871d6cead0bf5596fe537a2eaa395a19f43e90},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.15352. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Ground-truth human joint torque estimation relies on motion capture systems, which suffer from limited outdoor usability and significant deployment expenses. Furthermore, direct scaling of ground-truth joint torques to obtain motor torque commands is not necessarily the optimal strategy. To address the aforementioned limitations, inspired by the human motion generation process, this paper proposes a novel assistance torque estimation method based on the dynamic model. From an optimization perspective, the proposed method directly generates motor-assist torque and lowers the cost of data acquisition. Then, a data-driven assistance torque prediction network is trained to enable accurate real-time prediction under complex outdoor environments. Experimental results demonstrate that optimized (estimated) assistance torque exhibits better phase consistency with gait trajectories and better alignment with task characteristics. Relative to the Zero torque condition, the predicted torque can decrease metabolic rate by 11.8%-17.7%, heart rate by 8.9%-14.3%, and peak muscle activation levels by 28.2%-54.0%, respectively. This provides a new perspective for low-cost adaptive exoskeleton assistance.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-15 in cs.RO).
Categorie arXiv: Robotics (cs.RO).
Pagina del lavoro: https://arxiv.org/abs/2609.15352 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-16T01:52:32.367Z dalla pagina https://arxiv.org/catchup/cs.RO/2026-09-15?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).