Author

Yuchen Liu

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Preprint Oct 2026

What Should Embodied Agents Remember? Benchmarking and Evaluating Memory Modality for Embodied Agents

Memory allows large language model (LLM) agents to use past experience, often stored as textual records, when responding to requests. For embodied agents that control robots, visual and spatial observations introduce a broader choice of memory modalities, affecting both the information available for subsequent tasks an...

Jun-Xiang Wang, Yu-Chen Liu, Yi Wu et al. · 0 citations

#artificial intelligence Preprint Sep 2026

CE$^4$L: Continual Ego, Exo, and Ego-Exo Learning

This work introduces Continual Ego, Exo, and Ego-Exo Learning (CE$^4$L), a unified multi-view CL benchmark spanning four representative tasks: cross-view referenced skill assessment, temporal action segmentation, cross-view association, and action anticipation&planning.

Hong-Wei Yan, Kang-Lei Zhou, Yu-Chen Liu et al. · 0 citations

#artificial intelligence Preprint Sep 2026

HINT-Plan: Human Intention-Aware Robot Task Planning in Context-Rich Environments using Vision Language Models

HINT-Plan employs Vision Language Models to anticipate high-level human intentions from third-person image observations, convert them into goal states, and solve joint task-planning problems, and shows the effectiveness of explicitly incorporating inferred human intentions into formal multi-agent task planning for proa...

Yu-Chen Liu, Luigi Palmieri, Lujun Li et al. · 0 citations

Preprint Aug 2026

Traj-LeWM: Path-Aware World-Model Planning via Latent Trajectory Cost

Traj-LeWM is proposed, which retains LeWM's local-dynamics objective and endpoint score while introducing a goal-conditioned latent trajectory cost (LTC) that aggregates trajectory-level information as a complementary signal.

Xiaodi Huang, Ziyi Ding, Jingtian Wan et al. · 3 citations