Adaptive multi-mode locomotion for bipedal wheel-legged robots via sparse mixture-of-experts deep reinforcement learning

TL;DR

This paper introduces a new control framework for bipedal wheel-legged robots using reinforcement learning to smoothly switch between wheel and leg movement.

Contribution

A novel MoE-based reinforcement learning framework with sparse activation for adaptive multi-mode locomotion in bipedal wheel-legged robots.

Findings

The MoE-enhanced algorithm shows improved training stability and higher rewards compared to single-network PPO.

The robot successfully transitions between rolling and leg-lifting gaits depending on terrain.

The method achieves a higher success rate in navigating diverse terrains.

Abstract

The bipedal wheel-legged robot combines the high energy efficiency of wheeled movement with the terrain adaptability of legged locomotion. However, achieving a smooth transition between these two heterogeneous motion modes within a unified control framework remains challenging. This study proposes a reinforcement learning control framework that integrates the Mixture of Experts (MoE) architecture. This approach employs a “divide and conquer” strategy by introducing a dynamic gating network and a Top-K sparse activation mechanism, which automatically allocates different motion modes to specific expert subnetworks, effectively decoupling conflicting gradients. Simulation results demonstrate that, compared to the single-network PPO method, the MoE-enhanced algorithm exhibits significant improvements in training stability and rewards. The learned policy successfully achieved smooth rolling on flat surfaces and transitioned to dynamic leg-lifting gaits when confronted with obstacles. In various test terrains, it showed a markedly higher success rate compared to the single-network PPO method.

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