Learning to Adapt through Bio-Inspired Gait Strategies for Versatile Quadruped Locomotion (2412.09440v3)

Abstract: Legged robots must adapt their gait to navigate unpredictable environments, a challenge that animals master with ease. However, most deep reinforcement learning (DRL) approaches to quadruped locomotion rely on a fixed gait, limiting adaptability to changes in terrain and dynamic state. Here we show that integrating three core principles of animal locomotion-gait transition strategies, gait memory and real-time motion adjustments enables a DRL control framework to fluidly switch among multiple gaits and recover from instability, all without external sensing. Our framework is guided by biomechanics-inspired metrics that capture efficiency, stability and system limits, which are unified to inform optimal gait selection. The resulting framework achieves blind zero-shot deployment across diverse, real-world terrains and substantially significantly outperforms baseline controllers. By embedding biological principles into data-driven control, this work marks a step towards robust, efficient and versatile robotic locomotion, highlighting how animal motor intelligence can shape the next generation of adaptive machines.

• The paper introduces a novel deep reinforcement learning framework that leverages bio-inspired gait strategies to achieve highly adaptive and versatile quadruped locomotion.
• The framework incorporates bio-inspired adaptive gait transition, procedural gait memory, and metrics-driven selection to enable robots to dynamically adjust locomotion across varied terrains.
• Extensive testing shows the system's zero-shot adaptability on complex terrains, demonstrating robust performance, improved stability, and emergence of animal-like transition phases.

Learning to Adapt: Bio-Inspired Gait Strategies for Versatile Quadruped Locomotion

The paper "Learning to Adapt: Bio-Inspired Gait Strategies for Versatile Quadruped Locomotion" explores advancements in quadrupedal robotics through a novel deep reinforcement learning (DRL) framework inspired by the adaptability of animal locomotion. This work targets the inherent constraint of existing DRL methods, which often lack adaptability and are limited to static locomotion strategies. The authors propose a bio-inspired model incorporating animal-like gait transition strategies, procedural gait memory, and adaptive motion adjustments to overcome these limitations.

Framework and Methodology

The authors introduce a DRL-based control framework built upon the gait selection policy ($\pi_{G}$), bio-inspired gait scheduler (BGS), and locomotion policy ($\pi_{L}$). The gait selection policy employs biomechanics metrics like cost of transport (CoT), torque saturation, and external work, modified to suit robotic applications. These metrics serve as the optimization criteria during the training of the $\pi_{G}$ policy, which selects the most appropriate gait for minimizing these metrics, reflective of animal gait transitions. Notably, $\pi_{L}$ and $\pi_{G}$ are trained using proximal policy optimization (PPO) with careful domain randomisation to facilitate robust sim-to-real transfer.

Key Features and Contributions

1. Adaptive Gait Transition: The proposed framework realizes real-time adaptive gait transitions by implementing gait transition strategies similar to those exhibited in animals. By varying the gait according to environmental complexity and terrain, the system surpasses previous DRL algorithms limited to single gait responses.
2. Procedural Gait Memory: The authors draw inspiration from the cerebellum's role in coordinating animal limb movement, using BGS to provide state-dependent pseudo procedural memory within the robot's observation space. This aids in effective deployment of diverse gait patterns under varied terrains without terrain-specific training.
3. Metrics-Driven Gait Selection: The paper posits a unified approach for gait selection through the minimization of several biomechanics metrics. This synthesis of metrics is shown to reproduce complex animal gait transition behaviors, such as the modulation of stride frequency during velocity changes, which prior research has solely attributed to single metrics.

Results and Implications

The effectiveness of the proposed system is empirically validated through extensive simulation and hardware experiments. The system's gaits, managed by the bio-inspired scheduler, exhibit high adaptability during zero-shot deployment across complex terrains including uneven grasslands and low-friction surfaces. This adaptability extends beyond typical DRL applications, capable of stability recovery through auxiliary gait maneuvers during critical perturbations—a capability previously unmodeled in existing frameworks.

Specifically, the evaluation results revealed:

• A decrease in velocity and contact schedule tracking error on flat and rough terrains compared to baseline methodologies.
• The emergence of animal-like transition phases characterized by gait mixing to minimize energy expenditure and increase stability.
• A robust handling of energy costs, actuator-structural forces, and stability metrics across terrains not encountered in training phases, further showcasing this framework's generality.

Future Directions and Developments

While this paper presents a significant advancement in adaptive robotic locomotion, it opens several avenues for future exploration:

• Integrating sensor feedback pertaining to external environmental features, potentially enhancing obstacle anticipation and energy-efficient path planning.
• Further exploration of biomechanics-inspired metrics might reveal additional insights into animal gait dynamics, which can be applied to enhance robotic gait adaptability.
• Expanding the framework to encompass multi-legged or hybrid locomotion systems could further extend this bio-inspired adaptability principle across various robotic platforms.

In conclusion, this research represents a substantial contribution to quadrupedal locomotion, offering insights into both the mechanical and computational aspects of bio-inspired robotics. It lays a promising groundwork for future enhancements in robotic adaptability through careful mimicry of natural biomechanical systems.