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AutoCGP: Closed-Loop Concept-Guided Policies from Unlabeled Demonstrations

Published in ICLR, 2025

AutoCGP: Closed-Loop Concept-Guided Policies from Unlabeled Demonstrations

AutoCGP is a framework for training robots to perform complex manipulation tasks by automatically discovering and using “manipulation concepts” (motor skills like grasping, pushing, etc.) without human annotations.

Recommended citation: Zhou, Pei, et al. "AutoCGP: Closed-Loop Concept-Guided Policies from Unlabeled Demonstrations." The Thirteenth International Conference on Learning Representations. https://openreview.net/pdf?id=9ehJCZz4aM

HiMaCon: Discovering Hierarchical Manipulation Concepts from Unlabeled Multi-Modal Data

Published in NeurIPS, 2025

HiMaCon: Discovering Hierarchical Manipulation Concepts from Unlabeled Multi-Modal Data

HiMaCon is a self-supervised framework that learns hierarchical “manipulation concepts” from unlabeled multi-modal robot demonstrations without human annotation.

Recommended citation: Liu, Ruizhe, et al. "$\textit {HiMaCon:} $ Discovering Hierarchical Manipulation Concepts from Unlabeled Multi-Modal Data." The Thirty-ninth Annual Conference on Neural Information Processing Systems. https://arxiv.org/pdf/2510.11321

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