A 3B-parameter open-source unified decoder-only transformer for general robot control that jointly handles perception, planning, reasoning, and action via interleaved vision-text-action pretraining on a 1.5M-sample dataset. Achieves strong results across manipulation tasks and benchmarks with fully open-sourced model, code, and dataset.

Introduces Video-Action Models (VAMs) that replace standard VLMs in VLAs with a pretrained internet-scale video backbone (Cosmos-Predict2) combined with a flow-matching action decoder. Achieves approximately 10× better sample efficiency on real-world pick-and-place tasks.

An open-source, sub-$500 7-DOF 3D-printed robotic arm with 1.2 kg payload, wrist and head cameras, and full LeRobot integration, designed to lower the barrier to entry for real manipulation research.

A 4D embodied world model that predicts RGB, depth, and optical flow from RGB-D input and instructions using a tri-branch diffusion architecture. Enables closed-loop bimanual robot control by bridging world prediction and policy execution.

A digital teleoperation system that uses hand-pose streams to drive a world model for real-time (40+ FPS) high-fidelity robot video rendering from a single image. Removes physical robots from the training loop entirely.

A lightweight plug-in that monitors latent visual dynamics in action-chunked policies, drops stale actions on drift, and replans on-the-fly achieving +17.7 success points while keeping the VLA backbone frozen.

A feed-forward model that decomposes unposed images into instance-structured 3D token groups without annotations, enabling unified reconstruction, segmentation, and manipulation.

A proprioceptive contact feedback method leveraging motor current signals as a touch proxy to enable compliant dexterous manipulation without dedicated tactile sensors.

Uses weight-space arithmetic to isolate domain shifts from task knowledge, enabling one-shot VLA policy adaptation to new cameras or embodiments.

Introduces a benchmark showing VLAs lose 20–40 points in commonsense/world knowledge versus source VLMs after robotics fine-tuning, evaluated via action-based answering.