Wenchao Ding

Robots can now perform complex, contact-rich tasks with significantly smoother and more continuous motions by learning high-frequency action chunks in a latent space.

Autonomous vehicles can now better navigate occluded environments thanks to a new risk map that understands both traffic flow and collision potential.

Achieve robust robot manipulation across diverse viewpoints without camera calibration by synthesizing novel views with a geometry-aware video diffusion model.

Pocket-sized VLA models can now achieve state-of-the-art robot manipulation performance by pre-training on a curated multimodal dataset and injecting manipulation-relevant representations into the action space.

End-to-end driving models are surprisingly bad at using navigation, but a new framework shows how to inject it for SOTA results.

LLMs can now write better hardware verification code: CoverAssert boosts functional coverage by up to 15% by iteratively guiding LLMs with coverage feedback.

Humanoid robots can now traverse steps up to 20cm and gaps up to 80cm thanks to a contrastive learning approach that forces MoE experts to specialize on different terrain types.

Watch two robots hug and dance: Rhythm enables robust, physically plausible interactions between dual humanoids, bridging the gap from simulation to real-world deployment.

Robots that learn from their mistakes *while* navigating? SERP unlocks this by evolving the action model in-context during replanning, boosting success rates and cutting token costs.