Jiaming Liu

LaST-HD: A novel human-to-robot action learning paradigm that aligns human-hand and robot demonstrations in a shared latent reasoning space.

This paper compares the performance of quantum machine learning models with classical models and finds that the quantum models do not yet surpass classical baselines.

The paper constructs datasets and benchmarks to evaluate the performance of visual generators in handling open-ended requests, and proposes a teach-then-search co-training framework to improve their world-knowledge.

This paper introduces Positive--Direction Matching (PDM), a branch-aware on-policy distillation objective to address Negative Branch Asymmetry (NBA) in diffusion models under classifier-free guidance.

This paper organizes embodied data sources for multimodal foundation models into a pyramid, focusing on real-robot, UMI-style, egocentric and exocentric, simulation, and general vision-language data.

This paper presents a closed-loop framework called LAW that integrates language reasoning, action generation, and world modeling for long-horizon manipulation tasks.