Yue Chen

Yue Chen is currently a graduate student at Peking University with Agibot Lab advised by Professor Hao Dong.

My research interest is broadly in Robotics, 3D Computer Vision and large language models (LLMs), with particular interests in generalizable object manipulation.

Email: yuechen020614 [at] gmail.com

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We introduce DexGarmentLab, a realistic sim environment for bimanual dexterous garment manipulation. Based on this environment, we propose a new benchmark, an efficient data collection pipeline, and a novel policy framework that uses category-level visual correspondences for few-shot garment manipulation.

We introduce TrustRAG, a robust Retrieval-Augmented Generation (RAG) framework. It defends against corpus poisoning attacks by a two-stage mechanism: identifying potential attack patterns with K-means clustering and detecting malicious docs via self-assessment.

We study the novel task of cluttered garments manipulation using dense visual affordance, with generalization towards diverse states, and propose a novel adaptation module to reorganize cluttered garments into configurations conducive to manipulation.

We theoretically extend equivariant Markov kernels and simplify the condition of equivariant diffusion process, thereby significantly improving training efficiency for trajectory-level SE(3) equivariant diffusion policy in an end-to-end manner.

We propose EqvAfford framework, with novel designs to guarantee the SE(3) equivariance in point-level affordance learning for downstream robotic manipulation.