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Hanna Yurchyk

Doctorat - McGill
Superviseur⋅e principal⋅e
David Meger
Sujets de recherche
Apprentissage de représentations
Apprentissage par renforcement
Modèles probabilistes
Robotique
Systèmes dynamiques
Vision par ordinateur

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
2025-09-30
2025 IEEE-RAS 24th International Conference on Humanoid Robots (Humanoids) (publié)
doi.org
arxiv.org
Large Pre-Trained Models for Bimanual Manipulation in 3D
Hanna Yurchyk
Wei-Di Chang
Gregory Dudek
David Meger
We investigate the integration of attention maps from a pre-trained Vision Transformer into voxel representations to enhance bimanual roboti… (voir plus)c manipulation. Specifically, we extract attention maps from DINOv2, a self-supervised ViT model, and interpret them as pixel-level saliency scores over RGB images. These maps are lifted into a 3D voxel grid, resulting in voxel-level semantic cues that are incorporated into a behavior cloning policy. When integrated into a state-of-the-art voxel-based policy, our attention-guided featurization yields an average absolute improvement of 8.2% and a relative gain of 21.9% across all tasks in the RLBench bimanual benchmark.
2025-09-24
ArXiv (prépublication)
arxiv.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.
2024-12-22
ArXiv (prépublication)
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.
2024-12-22
ArXiv (prépublication)
arxiv.org
arxiv.org