Jingyuan Li

LM-X: Explainable Action Modeling with Progress, Event, and Uncertainty Prediction for Generalist Robot Manipulation

RoboCousin: Build Your Own Simulation Playground for Robust Bimanual Robotic Manipulation

Simulation and real-robot experiments show that the automatically generated interaction annotations are comparable to curated annotations, generated assets provide effective sim-to-real supervision, and tabletop cousins can improve transfer beyond training on a single reconstructed scene.

LM-X: Explainable Vision--Language--Action Modeling via Progress, Event, and Uncertainty Prediction

LM-X is introduced, which organizes prediction across task, event, and motor scales without claiming anatomical correspondence and shows that explicit multi-timescale predictive state can strengthen control while exposing interpretable internal estimates.

MotionPyramid: Controllable Motion Synthesis via Stylized Phase Manifolds

This work introduces stylized phase manifolds—a compact, interpretable latent representation that disentangles motion content, the temporal structure, and style and develops a diffusion‐based motion generator that enables fine‐grained control over semantic, temporal, and stylistic aspects of motion.