Multi-potential reward shaping improves humanoid robot learning reliability
MPBRS: Multi-Potential-Based Reward Shaping for Unified Reward Configuration in Humanoid Locomotion
RoboticsArtificial Intelligence
Summary
Teaching humanoid robots to walk and move is tricky because the way they learn depends a lot on the rewards they get for doing things right. The researchers found a new way to design these rewards using multiple guiding signals that help the robots explore without changing their ultimate goals. This method works consistently across different kinds of humanoid robots without needing to redesign rewards for each one. It also makes the learning process more stable and easier to set up.
What this means in practice
- •For robotics engineers: Implement a universal reward configuration to accelerate and simplify training of multiple humanoid robot types without per-robot reward redesign.
- •For simulation platform developers: Integrate multi-potential-based rewards to provide consistent learning experiences across diverse humanoid robot models in simulators.
Authors
Van Chung Nguyen, Hung Manh La
Abstract
Designing and tuning reward functions remains one of the main challenges in building effective reinforcement learning (RL) pipelines for humanoid locomotion. While shaping rewards can accelerate learning, naively formulated terms often conflict with the desired behavior, overfit to a specific robot, or become unstable under small weight changes. Potential-Based Reward Shaping (PBRS) offers an alternative solution: shaping the reward via differences of a potential function to guide exploration without changing the optimal policy. In this paper, we study Multi-PBRS for humanoid locomotion and propose a unified reward configuration. We then evaluate the same reward structure and weights across nine humanoid robots, requiring only robot-specific metadata (joint/link mappings and default poses) rather than per-robot reward redesign. Our experiments show that, compared to standard shaping, MPBRS provides more consistent scaling behavior and reduced tuning sensitivity across robots, making it easier to tune without altering the optimal policy.