Uses language and demonstrations to learn which aspects of behavior matter for reward in imitation learning, enabling 5× faster learning by avoiding copying irrelevant motion details. Accepted at ICRA 2026.

Steers frozen robot foundation models including VLAs, diffusion policies, and world models using human corrections via action inversion in latent space without fine-tuning the base policy. Outperforms supervised fine-tuning and latent RL with only 5–20 human interventions.

Presents a single end-to-end policy that performs precise, unscripted assembly of complex objects, measuring every grasp and alignment without speed-ups or human intervention.

38B autoregressive world foundation model unifying text-to-image, multi-view scene generation, embodied transfer, and video generation; ranks #1 on World Arena.

A spatial-aware point prediction method for embodied localization tasks. Addresses grounding and spatial reasoning in embodied AI settings.

An embodied AI framework for multi-AUV (Autonomous Underwater Vehicle) multi-target tracking in ad-hoc underwater networks. Incorporates scene-adaptive policies for dynamic underwater environments.

Proposes a plug-and-play policy module designed to accelerate existing embodied manipulation policies without retraining them from scratch.

Introduces a real-time 3D-aware policy framework for embodied manipulation tasks, enabling spatially grounded action generation.

Presents a vision-language-action model grounded in 3D Gaussian representations that integrates geometric and semantic awareness for improved robotic manipulation.

Leverages symmetric Koopman operator predictions within a reinforcement learning framework to achieve faster training and better generalization for legged robot locomotion.