Portrait of Hsiu-Chin Lin

Hsiu-Chin Lin

Associate Academic Member
Assistant Professor, McGill University, School of Computer Science
Research Topics
Autonomous Robotics Navigation
Climate Change
Deep Learning
Out-of-Distribution (OOD) Detection
Reinforcement Learning
Robotics

Biography

Hsiu-Chin Lin is an assistant professor at the School of Computer Science and in the Department of Electrical and Computer Engineering at McGill University.

Her research spans model-based motion control, optimization and machine learning for motion planning. She is particularly interested in adapting robot motion in dynamic environments for manipulators and quadruped robots.

Lin was formerly a research associate at the University of Edinburgh and the University of Birmingham. Her PhD research at the University of Edinburgh was on robot learning.

Current Students

Amin Abyaneh
PhD - McGill University
Website
Google Scholar
Elham Daneshmand
PhD - McGill University
Co-supervisor :
Glen Berseth
Mohamad H. Danesh
PhD - McGill University
Website
Github
Google Scholar
Anas Houssaini
Master's Research - McGill University
Principal supervisor :
David Meger
Github
Zina Kamel
Master's Research - McGill University
Principal supervisor :
David Meger
Rafid Saif
Master's Research - McGill University
Github
Google Scholar

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
Contractive Diffusion Policies: Robust Action Diffusion via Contractive Score-Based Sampling with Differential Equations
Amin Abyaneh
Charlotte Morissette
Mohamad H. Danesh
Anas El Houssaini
David Meger
Gregory Dudek
Hsiu-Chin Lin
Diffusion policies have emerged as powerful generative models for offline policy learning, whose sampling process can be rigorously characte… (see more)rized by a score function guiding a Stochastic Differential Equation (SDE). However, the same score-based SDE modeling that grants diffusion policies the flexibility to learn diverse behavior also incurs solver and score-matching errors, large data requirements, and inconsistencies in action generation. While less critical in image generation, these inaccuracies compound and lead to failure in continuous control settings. We introduce Contractive Diffusion Policies (CDPs) to induce contractive behavior in the diffusion sampling dynamics. Contraction pulls nearby flows closer to enhance robustness against solver and score-matching errors while reducing unwanted action variance. We develop an in-depth theoretical analysis along with a practical implementation recipe to incorporate CDPs into existing diffusion policy architectures with minimal modification and computational cost. We evaluate CDPs for offline learning by conducting extensive experiments in simulation and real-world settings. Across benchmarks, CDPs often outperform baseline policies, with pronounced benefits under data scarcity.
2026-01-01
arXiv (Cornell University) (preprint)
doi.org
arxiv.org
Piezoelectric tuning of thermal conductivity in nano-architected gallium nitride metamaterials
Jun Cai
Alireza Seyedkanani
Benyamin Shahryari
Hsiu-Chin Lin
Abdolhamid Akbarzadeh
2025-12-31
International Journal of Heat and Mass Transfer (published)
doi.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.
2025-07-08
ArXiv (preprint)
doi.org
arxiv.org
Speciation of coral-associated barnacles: generalists versus specialists in the Indo-West Pacific
Hsiu-Chin Lin
Lorenzo C. Halasan
Yoko Nozawa
Benny Kwok Kan Chan
2025-07-01
Coral reefs (published)
doi.org
Genetic Analysis of Polyunsaturated Fatty Acids Biosynthesis Pathway Determines Four Distinct Thraustochytrid Types.
Sou-Yu Cheng
Yi-Jing Chen
Hsiu-Chin Lin
Hsin-Yang Chang
Ming-Der Huang
2025-04-01
Environmental Microbiology (published)
doi.org
Genetic Analysis of Polyunsaturated Fatty Acids Biosynthesis Pathway Determines Four Distinct Thraustochytrid Types
Sou‐Yu Cheng
Yi‐Jing Chen
Hsiu-Chin Lin
Hsin‐Yang Chang
Ming‐Der Huang
ABSTRACT Thraustochytrids, diverse marine unicellular protists encompassing over 10 recognised genera, are renowned for synthesising polyuns… (see more)aturated fatty acids (PUFAs), with content and composition varying substantially across genera. While PUFAs are known to be produced via PUFA synthase (PUFA‐S) and/or elongase/desaturase (ELO/DES) pathways, the distinctions in genes involved remain unexplored. This study analysed PUFA biosynthetic genes in 19 thraustochytrid strains across six genera, categorising them into four types. Type I exclusively utilises the ELO/DES pathway, Type II employs both PUFA‐S and complete ELO/DES pathways, while Types III and IV primarily rely on PUFA‐S, with Type III lacking the canonical Δ9 desaturase and Type IV missing most desaturase and elongase enzymes. Notably, the Δ9 desaturase and ATP‐citrate lyase (ACLY) are exclusive to Types I and II, while β‐carotene hydroxylase (CrtZ) is absent in these types. ACLY absence suggests alternative acetyl‐CoA supply pathways in Types III and IV, whereas CrtZ absence implies either a lack of specific xanthophylls or alternative biosynthetic pathways in Types I and II. Synteny analysis revealed conserved genomic organisation of PUFA biosynthetic genes, indicating a shared evolutionary trajectory. This study provides insights into the genetic diversity underlying PUFA biosynthesis in thraustochytrids, while proposing putative evolutionary pathways for the four lineages.
2025-03-28
Environmental Microbiology (published)
doi.org
Contractive Dynamical Imitation Policies for Efficient Out-of-Sample Recovery
Amin Abyaneh
Mahrokh Ghoddousi Boroujeni
Hsiu-Chin Lin
Giancarlo Ferrari-Trecate
Imitation learning is a data-driven approach to learning policies from expert behavior, but it is prone to unreliable outcomes in out-of-sam… (see more)ple (OOS) regions. While previous research relying on stable dynamical systems guarantees convergence to a desired state, it often overlooks transient behavior. We propose a framework for learning policies using modeled by contractive dynamical systems, ensuring that all policy rollouts converge regardless of perturbations, and in turn, enable efficient OOS recovery. By leveraging recurrent equilibrium networks and coupling layers, the policy structure guarantees contractivity for any parameter choice, which facilitates unconstrained optimization. Furthermore, we provide theoretical upper bounds for worst-case and expected loss terms, rigorously establishing the reliability of our method in deployment. Empirically, we demonstrate substantial OOS performance improvements in robotics manipulation and navigation tasks in simulation.
2025-01-22
ICLR.cc/2025/Conference (poster)
doi.org
openreview.net
Contractive Dynamical Imitation Policies for Efficient Out-of-Sample Recovery
Amin Abyaneh
Mahrokh Ghoddousi Boroujeni
Hsiu-Chin Lin
Giancarlo Ferrari-Trecate
Imitation learning is a data-driven approach to learning policies from expert behavior, but it is prone to unreliable outcomes in out-of-sam… (see more)ple (OOS) regions. While previous research relying on stable dynamical systems guarantees convergence to a desired state, it often overlooks transient behavior. We propose a framework for learning policies using modeled by contractive dynamical systems, ensuring that all policy rollouts converge regardless of perturbations, and in turn, enable efficient OOS recovery. By leveraging recurrent equilibrium networks and coupling layers, the policy structure guarantees contractivity for any parameter choice, which facilitates unconstrained optimization. Furthermore, we provide theoretical upper bounds for worst-case and expected loss terms, rigorously establishing the reliability of our method in deployment. Empirically, we demonstrate substantial OOS performance improvements in robotics manipulation and navigation tasks in simulation.
2024-12-10
ArXiv (preprint)
arxiv.org
arxiv.org
A new species of Hoplostethus from Sumatra, eastern Indian Ocean, with comments on its most similar congeners (Trachichthyiformes: Trachichthyidae).
Yo Su
Alexander N. Kotlyar
Hsiu-Chin Lin
Toshio Kawai
HSUAN-CHING HO
2024-11-08
Journal of Fish Biology (published)
doi.org
A new species of Hoplostethus from Sumatra, eastern Indian Ocean, with comments on its most similar congeners (Trachichthyiformes: Trachichthyidae).
Yo Su
Alexander N. Kotlyar
Hsiu-Chin Lin
Toshio Kawai
HSUAN-CHING HO
2024-11-08
Journal of Fish Biology (published)
doi.org
Single-Shot Learning of Stable Dynamical Systems for Long-Horizon Manipulation Tasks
Alexandre St-Aubin
Amin Abyaneh
Hsiu-Chin Lin
Mastering complex sequential tasks continues to pose a significant challenge in robotics. While there has been progress in learning long-hor… (see more)izon manipulation tasks, most existing approaches lack rigorous mathematical guarantees for ensuring reliable and successful execution. In this paper, we extend previous work on learning long-horizon tasks and stable policies, focusing on improving task success rates while reducing the amount of training data needed. Our approach introduces a novel method that (1) segments long-horizon demonstrations into discrete steps defined by waypoints and subgoals, and (2) learns globally stable dynamical system policies to guide the robot to each subgoal, even in the face of sensory noise and random disturbances. We validate our approach through both simulation and real-world experiments, demonstrating effective transfer from simulation to physical robotic platforms. Code is available at https://github.com/Alestaubin/stable-imitation-policy-with-waypoints
2024-10-01
ArXiv (preprint)
doi.org
arxiv.org