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Stanley Wu

Alumni

Publications

VOCALoco: Viability-Optimized Cost-aware Adaptive Locomotion
Stanley Wu
Mohamad H. Danesh
Simon Li
Hanna Yurchyk
Amin Abyaneh
Anas El Houssaini
David Meger
Hsiu-Chin Lin
Recent advancements in legged robot locomotion have facilitated traversal over increasingly complex terrains. Despite this progress, many ex… (see more)isting 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
2026-01-31
IEEE Robotics and Automation Letters (published)
doi.org
arxiv.org
Safe Domain Randomization via Uncertainty-Aware Out-of-Distribution Detection and Policy Adaptation
Mohamad H. Danesh
Maxime Wabartha
Stanley Wu
Joelle Pineau
Hsiu-Chin Lin
Deploying reinforcement learning (RL) policies in real-world involves significant challenges, including distribution shifts, safety concerns… (see more), and the impracticality of direct interactions during policy refinement. Existing methods, such as domain randomization (DR) and off-dynamics RL, enhance policy robustness by direct interaction with the target domain, an inherently unsafe practice. We propose Uncertainty-Aware RL (UARL), a novel framework that prioritizes safety during training by addressing Out-Of-Distribution (OOD) detection and policy adaptation without requiring direct interactions in target domain. UARL employs an ensemble of critics to quantify policy uncertainty and incorporates progressive environmental randomization to prepare the policy for diverse real-world conditions. By iteratively refining over high-uncertainty regions of the state space in simulated environments, UARL enhances robust generalization to the target domain without explicitly training on it. We evaluate UARL on MuJoCo benchmarks and a quadrupedal robot, demonstrating its effectiveness in reliable OOD detection, improved performance, and enhanced sample efficiency compared to baselines.
2025-07-07
arXiv (preprint)
doi.org
arxiv.org