2 citations · 3 across the 10 of their papers we have counts for
10 papers
Scaling Whole-Body Human Musculoskeletal Behavior Emulation for Specificity and Diversity
Yunyue Wei, Chenhui Zuo, Shanning Zhuang +3
The embodied learning of human motor control requires whole-body neuro-actuated musculoskeletal dynamics, while the internal muscle-driven processes underlying movement remain inac…
Scalable Exploration for High-Dimensional Continuous Control via Value-Guided Flow
Yunyue Wei, Chenhui Zuo, Yanan Sui
Controlling high-dimensional systems in biological and robotic applications is challenging due to expansive state-action spaces, where effective exploration is critical. Commonly u…
Embodied Learning of Reward for Musculoskeletal Control with Vision Language Models
Saraswati Soedarmadji, Yunyue Wei, Chen Zhang +2
Discovering effective reward functions remains a fundamental challenge in motor control of high-dimensional musculoskeletal systems. While humans can describe movement goals explic…
Bipedal Balance Control with Whole-body Musculoskeletal Standing and Falling Simulations
Chengtian Ma, Yunyue Wei, Chenhui Zuo +2
Balance control is important for human and bipedal robotic systems. While dynamic balance during locomotion has received considerable attention, quantitative understanding of stati…
Motion Control of High-Dimensional Musculoskeletal Systems with Hierarchical Model-Based Planning
Yunyue Wei, Shanning Zhuang, Vincent Zhuang +1
Controlling high-dimensional nonlinear systems, such as those found in biological and robotic applications, is challenging due to large state and action spaces. While deep reinforc…
Safe Bayesian Optimization for the Control of High-Dimensional Embodied Systems
Yunyue Wei, Zeji Yi, Hongda Li +2
Learning to move is a primary goal for animals and robots, where ensuring safety is often important when optimizing control policies on the embodied systems. For complex tasks such…
Scalable Bayesian Optimization via Focalized Sparse Gaussian Processes
Yunyue Wei, Vincent Zhuang, Saraswati Soedarmadji +1
Bayesian optimization is an effective technique for black-box optimization, but its applicability is typically limited to low-dimensional and small-budget problems due to the cubic…
Improving sample efficiency of high dimensional Bayesian optimization with MCMC
Zeji Yi, Yunyue Wei, Chu Xin Cheng +2
Sequential optimization methods are often confronted with the curse of dimensionality in high-dimensional spaces. Current approaches under the Gaussian process framework are still…
An Invariant Information Geometric Method for High-Dimensional Online Optimization
Zhengfei Zhang, Yunyue Wei, Yanan Sui
Sample efficiency is crucial in optimization, particularly in black-box scenarios characterized by expensive evaluations and zeroth-order feedback. When computing resources are ple…
Safe Policy Optimization with Local Generalized Linear Function Approximations
Akifumi Wachi, Yunyue Wei, Yanan Sui
Safe exploration is a key to applying reinforcement learning (RL) in safety-critical systems. Existing safe exploration methods guaranteed safety under the assumption of regularity…