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

2026-06-17

AI Summary

This study addresses the challenge that existing bipedal robots fail to replicate the critical role of human toes in agility, energy efficiency, and impact absorption, and lack quantitative validation of active toe benefits. To bridge this gap, the authors develop a 14-degree-of-freedom anthropomorphic bipedal robot platform featuring a lightweight, high-torque, and robust active toe mechanism. Using a unified reinforcement learning training pipeline in a high-fidelity simulation environment, they present the first quantitative assessment of active toes’ comprehensive benefits under identical conditions. Experimental results demonstrate that, at a walking speed of 1.33 m/s, the active toe configuration reduces the cost of transport (CoT) by 17.5%, decreases heel-strike ground reaction forces by 5.0%, and lowers both the average and peak path deviations by 25.0% and 34.0%, respectively, compared to a no-toe configuration.

Abstract

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.

Problem

• bipedal robots
• active toes
• agility
• efficiency
• impact absorption

Innovation

• active toes
• bipedal robot
• high-fidelity simulation
• reinforcement learning
• impact absorption