Xiao-Quan Sun

Human tactile gloves costing under $11 can now generate robot-ready tactile data, slashing acquisition costs by over 95% and boosting efficiency 3.5x for dexterous manipulation.

Achieving 99.1% accuracy in object manipulation without the need for depth or additional 3D modules could revolutionize how robots interact with complex environments.

Hierarchical latent actions in HiMem-WAM boost long-horizon manipulation robustness, outperforming existing models in real-world scenarios.

LargeMonitor achieves robust, zero-shot drift detection and dynamic adaptation in online continual learning, outperforming traditional methods that are blind to the structural causes of data shifts.

Real-world robots forget how to fold towels after learning to pick-and-place, but this work shows experience replay can help, if you do it right.

Stop struggling with compounding errors in long-horizon robotic tasks: AtomVLA leverages LLMs and latent world models to decompose tasks and score actions, boosting success rates to 97% on LIBERO.

A practical VLA model, LLaVA-VLA, achieves strong generalization and versatility on a new benchmark, CEBench, while running on consumer-grade GPUs, eliminating the need for costly pre-training.