Robotics

CARP: A novel visuomotor policy learning paradigm achieves high accuracy and 10x faster inference than state-of-the-art by combining autoregressive efficiency and diffusion model precision through a c…

RAPL efficiently aligns robots with human preferences using minimal feedback by aligning visual representations before reward learning.

Moto: Bridging language for robot manipulation using latent motion tokens, achieving superior performance with limited data.

Code-as-Monitor (CaM) uses vision-language models and constraint-aware visual programming to achieve both reactive and proactive robotic failure detection in real-time, improving success rates and red…

WildLMa enables robots to perform complex, long-horizon manipulation tasks in unstructured environments by combining language-conditioned imitation learning, a whole-body controller for efficient tele…

SWIFT, a new system, enables a soft robotic hand to learn dynamic pen spinning via real-world trial-and-error, achieving 100% success across diverse pen properties without explicit object modeling.

DynaMem empowers robots with online dynamic spatio-semantic memory, achieving a 2x improvement in pick-and-drop success rate on non-stationary objects compared to static systems.

DeeR-VLA dynamically adjusts the size of a multimodal large language model based on task difficulty, significantly reducing computational cost and memory usage in robotic control without compromising …