Junjie He

Robust-WAM achieves superior out-of-distribution generalization in robot control by seamlessly integrating semantic foresight into action predictions while leveraging extensive VGM pretraining.

Achieving 98.0% success in cross-embodiment manipulation without manual action alignment could redefine how we approach robot control across diverse platforms.

MobileWAM achieves superior mobile manipulation performance by seamlessly integrating foresight into action planning, outpacing state-of-the-art methods.

Video can be reimagined as a dynamic interplay of stable contexts and evolving events, revolutionizing real-time interaction capabilities in AI.

Higher resolution in real-time audio-visual interactions can be achieved without sacrificing latency, enabling clearer agent representation in conversations.

Many robotic policies that seem successful in manipulation tasks actually compromise safety, with SoftVTBench revealing a stark contrast between goal completion and physical safety metrics.

Sub-second duplex audio-visual communication is now achievable with a single, unified model that eliminates the latency of traditional cascaded systems.

Ditch slow, multi-step video generation: S-VAM distills the structured generative priors of multi-step denoising into a single forward pass for real-time robot action prediction.