VOCALoco: Viability-Optimized Cost-aware Adaptive Locomotion

TL;DR

VOCALoco is a modular framework that dynamically selects safe and energy-efficient locomotion policies for legged robots based on terrain perception, improving robustness over traditional deep reinforcement learning methods.

Contribution

It introduces a novel skill-selection framework that evaluates policy viability and cost, enabling adaptive and safe locomotion over complex terrains.

Findings

Enhanced robustness in stair traversal

Improved safety during locomotion

Effective real-world deployment on quadrupedal robots

Abstract

Recent advancements in legged robot locomotion have facilitated traversal over increasingly complex terrains. Despite this progress, many existing approaches rely on end-to-end deep reinforcement learning (DRL), which poses limitations in terms of safety and interpretability, especially when generalizing to novel terrains. To overcome these challenges, we introduce VOCALoco, a modular skill-selection framework that dynamically adapts locomotion strategies based on perceptual input. Given a set of pre-trained locomotion policies, VOCALoco evaluates their viability and energy-consumption by predicting both the safety of execution and the anticipated cost of transport over a fixed planning horizon. This joint assessment enables the selection of policies that are both safe and energy-efficient, given the observed local terrain. We evaluate our approach on staircase locomotion tasks, demonstrating its performance in both simulated and real-world scenarios using a quadrupedal robot. Empirical results show that VOCALoco achieves improved robustness and safety during stair ascent and descent compared to a conventional end-to-end DRL policy