Hugging Face Daily PapersAmirhossein Kazemipour, Hehui Zheng, Robert KatzschmannSep 15, 20261 min readpaperadvanced

Fingers as Legs: Learning Self-Supported Locomotion and Manipulation with an Anthropomorphic Hand

Summary

The authors train an anthropomorphic robotic hand to crawl, steer, and recover from falls using its fingers for both support and manipulation, via a reinforcement‑learning reward formulation tuned to the hand's asymmetry. Sim‑to‑real experiments show faster locomotion than quadruped‑style rewards and successful untethered tasks without onboard vision.

A reward design that accounts for unequal finger actuation yields faster simulated locomotion than standard quadruped rewards.
Policies trained in a calibrated simulator transfer to hardware, enabling untethered crawling, steering, and fall recovery.
The same finger mechanisms serve dual roles: supporting body weight and manipulating objects, eliminating separate locomotion hardware.
Hardware runs fully onboard with no external vision; only overhead visual feedback is used for object‑pushing tasks.

Robotics engineers working on compact mobile manipulators will find the dual‑use finger approach and the RL pipeline valuable for reducing hardware complexity and improving autonomy.

8/10

reinforcement learning robotics hardware manipulation locomotion

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Fingers as Legs: Learning Self-Supported Locomotion and Manipulation with an Anthropomorphic Hand

Summary

Related reading

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From Pretraining to Proficiency: Real-World Subtask RL for Long-Horizon Manipulation with Minimal Human Intervention arxiv.org

HarnessVLN: Unifying Training-Free Embodied Navigation through an Agent Harness arxiv.org

Learning Foresight without Explicit Trajectories for 3D Diffusion Policies arxiv.org

Breaking the Vision-Action Shortcut: Latent Interface Training for Generalizable Robotics Foundation Models arxiv.org

In-Context Robot Learning with VLM Agents arxiv.org

RetireOPD: Self-Retiring On-Policy Distillation for Agentic Reinforcement Learning arxiv.org