Trains perception-driven dexterous manipulation policies in simulation for zero-shot real-world transfer on humanoid robots. Focuses on bridging the sim-to-real gap for vision-based dexterous tasks.

A framework enabling humanoid robots to execute diverse motions within physical limits, with video demonstrations and open-source code. Targets generalizable motion tracking across varied humanoid morphologies.

A pipeline combining active vision and high-resolution tactile sensing for dexterous manipulation on high-DoF robot hands, achieving approximately 2.5 minutes of continuous autonomous control on real-world tasks such as food preparation.

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.