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STRIDER: Stepping-Enabled Multi-Gait Hierarchical 3D Loco-Manipulation Framework for Humanoid Robots — arXiv:2609.23483

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.23483
License (as archived): CC0-1.0
Archived: 2026-10-09T22:23:58.026Z
SHA-256 of the archived text: 92d5a06ca220d553f13db98a51b6a90da05781fa340df8064f25d156bfc75f11
Stable URL of this page: https://metraipolis.com/documento/dc_ee95040f026b

How to cite

STRIDER: Stepping-Enabled Multi-Gait Hierarchical 3D Loco-Manipulation Framework for Humanoid Robots — arXiv:2609.23483 — arXiv (en), https://arxiv.org/abs/2609.23483 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ee95040f026b (Metraipolis archive dc_ee95040f026b, sha256 92d5a06ca220d553f13db98a51b6a90da05781fa340df8064f25d156bfc75f11)

BibTeX

@misc{dcee95040f026b,
  title = {STRIDER: Stepping-Enabled Multi-Gait Hierarchical 3D Loco-Manipulation Framework for Humanoid Robots — arXiv:2609.23483},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.23483},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ee95040f026b; sha256 92d5a06ca220d553f13db98a51b6a90da05781fa340df8064f25d156bfc75f11},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.23483. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Humanoid loco-manipulation faces two prominent limitations: controllers using continuous velocity commands cannot precisely regulate individual footholds, while specialized foothold-tracking modules are difficult to integrate with whole-body manipulation. Furthermore, standard action-based imitation distillation primarily transfers expert actions, without explicitly encouraging a shared representation of heterogeneous skills. This paper introduces STRIDER, a hierarchical multi-gait framework to bridge these gaps. The framework integrates terrain-aware 3D stepping logic, Adversarial Motion Priors (AMP)-based natural walking, and Cartesian upper-body control: its stepping expert selects feasible footholds in the stance-foot frame and generates clearance-aware swing trajectories. To fuse distinct walking and stepping experts into one executable student policy, we propose Latent Distillation Proximal Policy Optimization (LD-PPO), a distillation algorithm augmented with teacher-conditioned latent alignment. By jointly optimizing on-policy reinforcement learning, DAgger-based action reconstruction, and latent alignment, LD-PPO transfers expert actions while encouraging a shared skill representation across heterogeneous modes. Simulation and real-robot evaluations on the TianGong Omni humanoid show that LD-PPO outperforms vanilla distillation-PPO in foothold-tracking and posture-tracking accuracy. Deployed on hardware, STRIDER realizes multi-gait loco-manipulation with accurate foothold and end-effector tracking.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-22 in cs.RO).
Categorie arXiv: Robotics (cs.RO).
Pagina del lavoro: https://arxiv.org/abs/2609.23483 — 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 2026-10-09T21:59:59.581Z dalla pagina https://arxiv.org/catchup/cs.RO/2026-09-22?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).