12 papers
Reference-Augmented Learning for Precise Tracking Policy of Tendon-Driven Continuum Robots
Ziqing Zou, Ke Qiu, Haojian Lu +2
Tendon-Driven Continuum Robots (TDCRs) pose significant control challenges due to their highly nonlinear, path-dependent dynamics and non-Markovian characteristics. Traditional Jac…
Learning-Based Dynamics Modeling and Robust Control for Tendon-Driven Continuum Robots
Ziqing Zou, Ke Qiu, Fei Wang +3
Tendon-Driven Continuum Robots (TDCRs) pose significant modeling and control challenges due to complex nonlinearities, such as frictional hysteresis and transmission compliance. Th…
UniMM: A Unified Mixture Model Framework for Multi-Agent Simulation
Longzhong Lin, Xuewu Lin, Kechun Xu +4
Simulation plays a crucial role in assessing autonomous driving systems, where the generation of realistic multi-agent behaviors is a key aspect. In multi-agent simulation, the pri…
Learning A Simulation-based Visual Policy for Real-world Peg In Unseen Holes
Liang Xie, Hongxiang Yu, Kechun Xu +5
This paper proposes a learning-based visual peg-in-hole that enables training with several shapes in simulation, and adapting to arbitrary unseen shapes in real world with minimal…
Real-Time Minimum-Energy Operating-Point Tracking for Battery-Powered Micro DC Motors Under Dynamically Variable Loading
Tzu-Hsiang Huang, Haojian Lu, Hen-Wei Huang +1
Micro DC brushed motors are widely deployed in battery-powered biomedical systems, where limited energy budgets and variable physiological loading impose stringent efficiency and s…
Seeing to Act, Prompting to Specify: A Bayesian Factorization of Vision Language Action Policy
Kechun Xu, Zhenjie Zhu, Anzhe Chen +7
The pursuit of out-of-distribution generalization in Vision-Language-Action (VLA) models is often hindered by catastrophic forgetting of the Vision-Language Model (VLM) backbone du…