ECO: Energy-Constrained Optimization with Reinforcement Learning for Humanoid Walking
Achieving stable and energy-efficient locomotion is essential for humanoid robots to operate continuously in real-world applications. Existing MPC and RL approaches often rely on energy-related metrics embedded within a multi-objective optimization framework, which require extensive hyperparameter tuning and often result in suboptimal policies. To address these challenges, we propose ECO (Energy-Constrained Optimization), a constrained RL framework that separates energy-related metrics from rewards, reformulating them as explicit inequality constraints. This method provides a clear and interpretable physical representation of energy costs, enabling more efficient and intuitive hyperparameter tuning for improved energy efficiency. ECO introduces dedicated constraints for energy consumption and reference motion, enforced by the Lagrangian method, to achieve stable, symmetric, and energy-efficient walking for humanoid robots. We evaluated ECO against MPC, standard RL with reward shaping, and four state-of-the-art constrained RL methods. Experiments, including sim-to-sim and sim-to-real transfers on the kid-sized humanoid robot BRUCE, demonstrate that ECO significantly reduces energy consumption compared to baselines while maintaining robust walking performance. These results highlight a substantial advancement in energy-efficient humanoid locomotion. All experimental demonstrations can be found on the project website: https://sites.google.com/view/eco-humanoid.
6989adb6beecc443208d27de
Weidong Huang
claimed_verified
2026-02-09T14:31:28.409Z
65c0ae04b7db0ab09541beed
/avatars/d98cbcc3ad1669221c4a50eccfabc9d0.svg
Weidong Huang
Weidong-Huang
user
Weidong-Huang
6989adb6beecc443208d27df
Jingwen Zhang
6989adb6beecc443208d27e0
Jiongye Li
6989adb6beecc443208d27e1
Shibowen Zhang
6989adb6beecc443208d27e2
Jiayang Wu
6989adb6beecc443208d27e3
Jiayi Wang
6989adb6beecc443208d27e4
Hangxin Liu
6989adb6beecc443208d27e5
Yaodong Yang
claimed_verified
2026-03-06T13:57:57.706Z
686894eb283604c2808f7c81
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/BGuMtSS3BGNicQiojujUO.png
yd yang
yyang13
user
yyang13
6989adb6beecc443208d27e6
Yao Su
ECO: Energy-Constrained Optimization with Reinforcement Learning for Humanoid Walking
Achieving stable and energy-efficient locomotion is essential for humanoid robots to operate continuously in real-world applications. Existing MPC and RL approaches often rely on energy-related metrics embedded within a multi-objective optimization framework, which require extensive hyperparameter tuning and often result in suboptimal policies. To address these challenges, we propose ECO (Energy-Constrained Optimization), a constrained RL framework that separates energy-related metrics from rewards, reformulating them as explicit inequality constraints. This method provides a clear and interpretable physical representation of energy costs, enabling more efficient and intuitive hyperparameter tuning for improved energy efficiency. ECO introduces dedicated constraints for energy consumption and reference motion, enforced by the Lagrangian method, to achieve stable, symmetric, and energy-efficient walking for humanoid robots. We evaluated ECO against MPC, standard RL with reward shaping, and four state-of-the-art constrained RL methods. Experiments, including sim-to-sim and sim-to-real transfers on the kid-sized humanoid robot BRUCE, demonstrate that ECO significantly reduces energy consumption compared to baselines while maintaining robust walking performance. These results highlight a substantial advancement in energy-efficient humanoid locomotion. All experimental demonstrations can be found on the project website: https://sites.google.com/view/eco-humanoid.
Energy-constrained optimization framework separates energy metrics from rewards using Lagrangian method to achieve stable, energy-efficient humanoid robot locomotion with reduced hyperparameter tuning.
model predictive control reinforcement learning constrained optimization Lagrangian method energy-constrained optimization humanoid robotics sim-to-sim transfer sim-to-real transfer