Research Topics

We work on neuro-musculo-skeletal modeling and reinforcement learning for human motion control and interactive robotics.

Related areas

AI for Health, Machine Learning, Neural Engineering, Robotics

MS-Human-700

Musculoskeletal Model Neural Control Biomechanics

A whole-body human musculoskeletal model with 700 muscle-tendon units, built to capture realistic human movement at full-body scale. It serves as a biomechanically grounded platform for studying motor control and learning controllers for human-like motion.

FlyGM

Connectomics Whole-body Control Neural Model

A whole-brain connectomic graph model that enables whole-body locomotion control in the fruit fly. By grounding control in the connectome, it links biological neural circuitry to coordinated, embodied behavior.

Safe Optimization

Bayesian Optimization Safe Learning

Efficient black-box optimization under safety constraints, where unsafe evaluations must be avoided throughout the search. The methods balance exploration and constraint satisfaction so that performance improves without violating safety requirements.

Preference-based Optimization

Bayesian Optimization Preference Learning

Optimization driven by preference feedback rather than explicit numerical objectives, where users compare candidates instead of scoring them. This makes it well suited to tuning systems whose quality is hard to quantify but easy to judge.

High-dimensional Optimization

Bayesian Optimization High-dimensional

Scalable optimization methods for high-dimensional problems, where classical Bayesian optimization struggles. The work develops techniques that remain sample-efficient as the search space grows.

Preference-based Learning

Reinforcement Learning Exoskeleton Human-in-the-loop

A personalized gait optimization framework for lower-body exoskeletons that learns from human feedback. It adapts assistance to each user's preferences, improving comfort and walking performance during interaction.

Wheeled-legged Quadruped

Reinforcement Learning Legged Robotics Locomotion

Learning adaptive locomotion for wheeled-legged quadrupeds that combine wheels and legs for versatile movement. The controllers switch between rolling and stepping to traverse varied terrain efficiently.