# VOCALoco: Viability-Optimized Cost-aware Adaptive Locomotion

arXiv: https://arxiv.org/abs/2510.23997 · HTML: https://mlsanity.com/paper/2510.23997/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2025.

## Built on by (most cited first, 3 of 3)

- [Morphology-Conditioned World Model for Cross-Embodiment Quadrupedal Locomotion](https://mlsanity.com/paper/2604.08780/) (2026)
- [CTS-MoE: Implicit Terrain Adaptation via Mixture-of-Experts for Perceptive Locomotion](https://mlsanity.com/paper/2606.19633/) (2026)
- [CopyQuad: Quadrupedal Skill Transfer from a Single Side-View Video Without a Depth Model](https://mlsanity.com/paper/2610.16737/) (2026)

## Abstract (arXiv)

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