Paper Detail

Towards Miniature Humanoid Tele-Loco-Manipulation Using Virtual Reality and Reinforcement Learning

Nicolas Kosanovic, Jordan Dowdy, Jean Chagas Vaz

arxiv Score 9.6

Published 2026-07-22 · First seen 2026-07-23

General AI

Abstract

Full-sized humanoid robot capabilities have grown exponentially in recent years, aiming towards general-purpose deployment in human environments. A popular control method used by manufacturers utilizes Virtual Reality for upper-body teleoperation and Reinforcement Learning for lower-body balance and locomotion control. As a result, a single remote operator can see, manipulate, and navigate about a real, distant physical environment. This powerful control stack is often relegated to expensive full-sized robots, many of which are inaccessible to the research community. Miniature humanoids are more prevalent, but employ less biomimicry in their design (e.g. fewer sensors, Degrees of Freedom, etc) and lack similar developments. This paper describes a compliant full-body telepresence control stack developed from the ground up for miniature humanoids. Framework experimentation on ROBOTIS OP3 hardware showcases walking at speeds up to 0.45 m/s independent of arm motions. Tele-loco-manipulation is demonstrated via a cube relocation experiment with an expert human operator. On average, the teleoperated system moved 2 different 40 g cubes within 10 mins, walking a total distance of 5 m. Overall, the developed system shows potential for miniature humanoid tele-loco-manipulation.

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BibTeX

@article{kosanovic2026miniature,
  title = {Towards Miniature Humanoid Tele-Loco-Manipulation Using Virtual Reality and Reinforcement Learning},
  author = {Nicolas Kosanovic and Jordan Dowdy and Jean Chagas Vaz},
  year = {2026},
  abstract = {Full-sized humanoid robot capabilities have grown exponentially in recent years, aiming towards general-purpose deployment in human environments. A popular control method used by manufacturers utilizes Virtual Reality for upper-body teleoperation and Reinforcement Learning for lower-body balance and locomotion control. As a result, a single remote operator can see, manipulate, and navigate about a real, distant physical environment. This powerful control stack is often relegated to expensive ful},
  url = {https://arxiv.org/abs/2607.20399},
  keywords = {cs.RO, cs.HC, cs.LG, Teleoperation, Humanoid robot, Virtual reality, Telerobotics, Human–computer interaction},
  eprint = {2607.20399},
  archiveprefix = {arXiv},
}

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