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Author

Chao Liu

5 papers indexed here

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#small language model Preprint Sep 2026

Memorize, Adapt, Ignore: Diagnosing Robot Learning Mechanisms under Training Data Variation

This work examines both model behavior and internal representations, using the empirical neural tangent kernel (NTK) as the primary diagnostic tool, and develops practical guidance for designing DR schemes, selecting models, and detecting shortcut learning.

Ke Zhang, D. Sutherland, Chao Liu · 0 citations
#artificial intelligence Preprint Sep 2026

DiagGen: Agentic Generation of Deformable Assets with Sim-based Diagnostics for Robotic Simulation

DiagGen is presented, an agentic framework that turns a single in-the-wild image into a simulation-ready deformable asset through a generate--simulate--diagnose--refine loop, and shows that diagnostics provides useful repair cues and can moderately improve the quality of generated deformable assets.

Guan-Xiong Chen, Yi-Duo Qu, Qian-Jun Xia et al. · 1 citation
#small language model Preprint Sep 2026

WorldContact: A Contact-Centric World Model for Scalable Robot Learning

Adapting robots to new objects and tasks requires interaction experience that can be costly to obtain. We present WorldContact, a contact-centric world model for deformable-object manipulation, constructed from a limited set of high-quality trajectories to generate additional training data efficiently. It predicts obje...

Caoliwen Wang, Meng-Di Wang, Heng Zhang et al. · 0 citations
Jul 2026

Learning social norms enhances compatibility in dynamic human-AI coordination

It is indicated that formalizing tacit social norms into explicit, quantifiable principles can enable AI agents to achieve mutually beneficial coordination in dynamic interactions, supporting their more natural integration into human society.

Yi Yang, Siyuan Liu, Xin Gao et al. · 0 citations
#robotics Jul 2026

Agentic Real2Sim: Physics-based World Modeling with Vision-Language Agents

Agentic Real2Sim is introduced, a framework for generalized physical world modeling with vision-language agents, converting a real-world recording of object-robot interaction into a simulatable episodic twin which preserves observations, geometries, robot interactions, and object states.

Guan-Xiong Chen, Qian-Jun Xia, Jia-Wei Peng et al. · 0 citations

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