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Comparative Study on Agility, Efficiency, and Impact Absorption of Bipedal Robots with Active Toes

arXiv 2606.19699 ↗First posted 18 Jun 2026cs.RO cs.LG cs.SYcited by 0

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Abstract (arXiv)

Human legs exhibit high efficiency, agility, and impact absorption, with toes playing a crucial role in these capabilities. While many attempts have been made to implement human-like toes in robots, they have not fully replicated human characteristics nor rigorously validated their benefits. We propose a 14-DOF biped robot emulating human toes' lightweight, high-torque, robust nature. To quantitatively analyze the effectiveness of the active toes in terms of agility, efficiency, and impact absorption, we developed a high-fidelity simulation training environment that reflects actual actuators with coupled transmissions and accurate power consumption. To ensure a fair comparison between configurations with and without active toes, we designed a minimal RL reward function and applied an identical training procedure to both. The simulation results indicate that, at 1.33 m/s walking, the toe-equipped robot reduced CoT by 17.5% and heel-strike GRF by 5.0% compared with the toe-ablation configuration. On the agility test, average and maximum path deviation decreased by 25.0% and 34.0%, respectively.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 27 references that are in the map. First posted on arXiv 18 Jun 2026.

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References · 9

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2021How to Train Your Robot with Deep Reinforcement Learning; Lessons We've Learnedcited by 213
  3. 2024Berkeley Humanoid: A Research Platform for Learning-based Controlcited by 24
  4. 2024Learning-based legged locomotion; state of the art and future perspectivescited by 23
  5. 2023Torque-based Deep Reinforcement Learning for Task-and-Robot Agnostic Learning on Bipedal Robots Using Sim-to-Real Transfercited by 22
  6. 2021Robust Feedback Motion Policy Design Using Reinforcement Learning on a 3D Digit Bipedal Robotcited by 17
  7. 2024Deep Reinforcement Learning for Bipedal Locomotion: A Brief Surveycited by 7
  8. 2022Tello Leg: The Study of Design Principles and Metrics for Dynamic Humanoid Robotscited by 2
  9. 2026Learning Thermal-Aware Locomotion Policies for an Electrically-Actuated Quadruped Robotcited by 1

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