\(\mathcal{D(R,O)}\) Grasp: A Unified Representation of Robot and Object Interaction for Cross-Embodiment Dexterous Grasping

Zhenyu Wei*, Zhixuan Xu*, Jingxiang Guo, Yiwen Hou, Chongkai Gao, Zhehao Cai, Jiayu Luo, Lin Shao

In Submission to International Conference on Robotics and Automation (ICRA) 2025

Best Robotics Paper Award @ CoRL 2024 Workshop MAPoDeL

Oral Presentation @ CoRL 2024 Workshop LFDM

Oral Presentation @ CoRL 2024 Workshop MAPoDeL

Introduce a novel representation, \(\mathcal{D(R,O)}\) for dexterous grasping tasks. This interaction-centric formulation transcends conventional robot-centric and object-centric paradigms, facilitating generalization across diverse robotic hands and objects.

FLIP: Flow-Centric Generative Planning for General-Purpose Manipulation Tasks

Chongkai Gao, Haozhuo Zhang, Zhixuan Xu, Zhehao Cai, Lin Shao

Oral Presentation @ CoRL 2024 Workshop LEAP

Propose flow-centric generative planning (FLIP) as an interactive world model for general-purpose model-based planning for manipulation tasks.

ManiFoundation Model for General-Purpose Robotic Manipulation of Contact Synthesis with Arbitrary Objects and Robots

Zhixuan Xu*, Chongkai Gao*, Zixuan Liu*, Gang Yang*, Chenrui Tie, Haozhuo Zheng, Haoyu Zhou, Weikun Peng, Debang Wang, Tianrun Hu, Tianyi Chen, Zhouliang Yu, Lin Shao

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2024

Oral Presentation

Introduce a framework taking contact synthesis as a unified task representation that can generalizes over objects, robots, and manipulation tasks.

Diff-LfD: Contact-aware Model-based Learning from Visual Demonstration for Robotic Manipulation via Differentiable Physics-based Simulation and Rendering

Xinghao Zhu, Jinghan Ke, Zhixuan Xu, Zhixin Sun, Bizhe Bai, Jun Lv, Qingtao Liu, Yuwei Zeng, Qi Ye, Cewu Lu, Masayoshi Tomizuka, Lin Shao

Conference on Robot Learning (CoRL) 2023

Oral Presentation

Propose a pipeline to learn dexterous manipulation from human video demonstrations. It includes self-supervised pose and shape estimation via differentiable rendering and contact sequence generation via differentiable simulation.

Object-centric Inference for Language Conditioned Placement: A Foundation Model based Approach

Zhixuan Xu, Kechun Xu, Yue Wang, Rong Xiong

IEEE International Conference on Advanced Robotics and Mechatronics (ICARM) 2023

Propose a framework that enhances pre-trained LLMs and VLMs through few-shot residual learning in robotic placement tasks, improving generalization to new instructions and objects while increasing sample efficiency.

Conference Reviewer

• IEEE International Conference on Robotics and Automation (ICRA)

Journal Reviewer

• IEEE Robotics and Automation Letters (RA-L)