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Hanna Yurchyk

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… (voir plus)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 (publié)
doi.org
arxiv.org
Large Pre-Trained Models for Bimanual Manipulation in 3D
Hanna Yurchyk
Wei-Di Chang
Gregory Dudek
David Meger
2025-09-29
IEEE-RAS Conference on Humanoid Robots (publié)
doi.org
arxiv.org
Fairness in Reinforcement Learning with Bisimulation Metrics
Sahand Rezaei-Shoshtari
Hanna Yurchyk
Scott Fujimoto
Doina Precup
David Meger
Ensuring long-term fairness is crucial when developing automated decision making systems, specifically in dynamic and sequential environment… (voir plus)s. By maximizing their reward without consideration of fairness, AI agents can introduce disparities in their treatment of groups or individuals. In this paper, we establish the connection between bisimulation metrics and group fairness in reinforcement learning. We propose a novel approach that leverages bisimulation metrics to learn reward functions and observation dynamics, ensuring that learners treat groups fairly while reflecting the original problem. We demonstrate the effectiveness of our method in addressing disparities in sequential decision making problems through empirical evaluation on a standard fairness benchmark consisting of lending and college admission scenarios.
2024-12-21
ArXiv (prépublication)
doi.org
arxiv.org