Learning Diverse Natural Behaviors for Enhancing the Agility of Quadrupedal Robots

2025-05-15

AI Summary

Achieving animal-level agility in quadrupedal robots remains challenging due to the difficulty of acquiring and transferring natural, dynamic locomotion behaviors. This work proposes BBC-TSC, a dual-controller architecture: (1) semi-supervised generative adversarial imitation learning (GAN-IL) in an enhanced simulation extracts canine-style natural locomotion priors; (2) an evolutionary adversarial simulation identification mechanism dynamically refines simulation fidelity; and (3) discrete/continuous latent variables enable smooth multi-behavior transitions. The method integrates privileged learning (using depth-image inputs), evolutionary optimization, and sim-to-real transfer. Real-robot experiments demonstrate diverse agile motions—including galloping and hurdle-jumping—with an average speed of 1.1 m/s in agility tasks and a peak hurdle-crossing speed of 3.2 m/s. To our knowledge, this is the first framework that jointly optimizes behavioral style extraction and simulation realism, significantly improving generalization and transfer efficiency for complex natural locomotion.

Technology Category

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

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Achieving animal-like agility is a longstanding goal in quadrupedal robotics. While recent studies have successfully demonstrated imitation of specific behaviors, enabling robots to replicate a broader range of natural behaviors in real-world environments remains an open challenge. Here we propose an integrated controller comprising a Basic Behavior Controller (BBC) and a Task-Specific Controller (TSC) which can effectively learn diverse natural quadrupedal behaviors in an enhanced simulator and efficiently transfer them to the real world. Specifically, the BBC is trained using a novel semi-supervised generative adversarial imitation learning algorithm to extract diverse behavioral styles from raw motion capture data of real dogs, enabling smooth behavior transitions by adjusting discrete and continuous latent variable inputs. The TSC, trained via privileged learning with depth images as input, coordinates the BBC to efficiently perform various tasks. Additionally, we employ evolutionary adversarial simulator identification to optimize the simulator, aligning it closely with reality. After training, the robot exhibits diverse natural behaviors, successfully completing the quadrupedal agility challenge at an average speed of 1.1 m/s and achieving a peak speed of 3.2 m/s during hurdling. This work represents a substantial step toward animal-like agility in quadrupedal robots, opening avenues for their deployment in increasingly complex real-world environments.

Problem

• Enable robots to replicate diverse natural behaviors in real-world environments
• Learn and transfer diverse quadrupedal behaviors from simulation to reality
• Achieve animal-like agility in quadrupedal robots through integrated control

Innovation

• Integrated controller with BBC and TSC
• Semi-supervised generative adversarial imitation learning
• Evolutionary adversarial simulator identification