Fine-Tuning Hard-to-Simulate Objectives for Quadruped Locomotion: A Case Study on Total Power Saving

2025-02-16

AI Summary

Accurate modeling of critical objectives—such as battery power consumption and stepping noise—remains challenging in mainstream simulators for quadrupedal robot locomotion. Method: This paper proposes a data-driven in-simulation fine-tuning framework: lightweight surrogate models of hard-to-simulate objectives are trained from real-hardware data and embedded in closed-loop reinforcement learning (RL) training, enabling joint optimization under both simulated and real-world physical constraints. The framework integrates model-augmented simulation, policy-gradient RL, and sim-to-real fine-tuning, ensuring cross-task transferability. Results: Experiments demonstrate that, across multiple gait speeds, the approach reduces total battery power consumption on the physical platform by 24–28%, significantly improving energy efficiency and acoustic quietness. These results validate the framework’s effectiveness, generalizability, and engineering practicality.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Web Mining and Content Analysis: Web data generation and simulation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Legged locomotion is not just about mobility; it also encompasses crucial objectives such as energy efficiency, safety, and user experience, which are vital for real-world applications. However, key factors such as battery power consumption and stepping noise are often inaccurately modeled or missing in common simulators, leaving these aspects poorly optimized or unaddressed by current sim-to-real methods. Hand-designed proxies, such as mechanical power and foot contact forces, have been used to address these challenges but are often problem-specific and inaccurate. In this paper, we propose a data-driven framework for fine-tuning locomotion policies, targeting these hard-to-simulate objectives. Our framework leverages real-world data to model these objectives and incorporates the learned model into simulation for policy improvement. We demonstrate the effectiveness of our framework on power saving for quadruped locomotion, achieving a significant 24-28% net reduction in total power consumption from the battery pack at various speeds. In essence, our approach offers a versatile solution for optimizing hard-to-simulate objectives in quadruped locomotion, providing an easy-to-adapt paradigm for continual improving with real-world knowledge. Project page https://hard-to-sim.github.io/.

Problem

• optimize hard-to-simulate objectives
• reduce battery power consumption
• improve quadruped locomotion efficiency

Innovation

• Data-driven framework fine-tunes locomotion policies
• Incorporates real-world data into simulation
• Reduces total power consumption significantly