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Jing Zhang

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Preprint Oct 2026

What Limits Cross-Task Generalization in World Action Models?

Cross-task generalization remains difficult for world action models (WAMs), even when their predicted futures depict plausible manipulation. This gap can be attributed directly: we make this gap directly testable by decoupling a WAM into a world model and an inverse-dynamics model (IDM), then holding the IDM fixed whil...

Chen Zhao, Zi-Peng Ye, Yi-Han Lin et al. · 0 citations
Preprint Sep 2026

Enabling a Unified Cross-Domain Representation for Two-Finger Gripper Manipulation via Interaction-Centric Modeling

Achieving robust cross-embodiment generalization in imitation learning demands overcoming a critical representation flaw that inextricably entangles task semantics with hardware-specific visual geometry. We propose an interaction-centric framework that leverages the shared structure of two-finger grippers via a paramet...

Guan-Lin Li, Shi-Feng Bao, Yi-Han Zhao et al. · 0 citations
Preprint Sep 2026

RoboHarn-Evo: Evolving Hierarchical Physical Knowledge for Self-Improving Robotic Manipulation

RoboHarn-Evo is introduced, a dual-loop harness that evolves Hierarchical Physical Knowledge (HPK) from physical experience that demonstrates that physical interaction can be accumulated into reusable knowledge for improving subsequent manipulation.

Shi-Feng Bao, Fan-Ding Huang, Yi-Han Lin et al. · 0 citations
Preprint Sep 2026

RoboRecover: Benchmarking Robot Policy Recovery under Execution Deviations

Results show that initial-state performance does not determine recovery performance and policies exhibit different recovery strengths across scenarios, and RoboRecover establishes recovery from execution-induced intermediate states as a distinct dimension of robot policy evaluation.

Yang Li, Chen Zhao, Zhuo-Ran Wang et al. · 1 citation

ProcVLM: Learning Procedure-Grounded Progress Rewards for Robotic Manipulation

ProcVLM is presented, a progress-aware vision-language model that learns procedure-grounded progress as a dense reward signal for manipulation and improves embodied procedural reasoning and yields more discriminative trajectory-internal progress estimates than representative baselines.

You-He Feng, Han Shi, Haoyang Li et al. · 11 citations
Preprint Aug 2026

JEPA-WAM: Learning Vision-Language-Action Policies with Joint-Embedding World Modeling

JEPA-WAM, a latent WAM built in a pretrained V-JEPA space, which couples latent transition prediction with continuous action generation through a shared predictor, predicts a spatially structured joint current-future target that captures task-shared visual temporal structure between current and future observations, whi...

Yi-Han Lin, Jia-Wei He, Shi-Feng Bao et al. · 12 citations · ⚡1

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