VL-GRiP3: A hierarchical pipeline leveraging vision-language models for autonomous robotic 3D grasping

Robots in dynamic, human-centric environments must follow language instructions while maintaining real-time reactive control. Vision-language-action (VLA) models offer a promising framework, but they assume temporally aligned reasoning and control.

Embodied intelligence is often studied through specialized models for individual tasks such as manipulation or navigation, resulting in fragmented capabilities and limited generalization across tasks, environments, and robot embodiments.

VLAbot: A human Vision–Language–Action models interaction framework for robotic assembly

With advances in robotic manipulation in recent years, tactile sensing has become increasingly important in scenarios where visual information is unreliable or insufficient.

Proposes a new flow-matching approach for robot action generation that improves efficiency in learning and repurposing generalist policies. Targets improved scalability and adaptability of robot action models.

An embodied data engine releasing 150 hours of synchronized egocentric video, full-body motion, hand tracking, object trajectories, audio, and tactile signals from everyday household activities.

A vision-tactile-language-action model that predicts latent tactile tokens for fine-grained contact-rich manipulation, achieving wins on all 9 real-robot tasks and 63.8% on 20 simulation tasks.

Equips foundation models with persistent, dynamically evolving native memory state and gradient-free online updates, enabling continual adaptation without retraining.

Achieves high-speed inference for vision-language-action policies at 32 Hz on an RTX 4090 with less than 1 GB VRAM using a 0.2B parameter model, enabling real-time VLA deployment on consumer hardware.