Grabette: an open system to record robot-manipulation data

HumanEgo: Zero-Shot Robot Learning from Minutes of Human Egocentric Videos

HumanEgo is a framework that enables zero‑shot robot learning from short egocentric human videos by converting each demonstration into an entity‑level hand‑object interaction representation and training a flow‑matching policy with dense auxiliary objectives. The method is robot‑data‑free, hardware‑agnostic, and data‑efficient, achieving 92.5 % success on four real‑world tasks with only 30 minutes of human video per task and outperforming matched‑time robot teleoperation by 41 %. HumanEgo also robustly transfers zero‑shot across new robots, cameras, and environments, and is released as an open‑source tool for learning robot policies directly from human data.

What Matters When Cotraining Robot Manipulation Policies on Everyday Human Videos?

arXiv:2606. 06627v1 Announce Type: cross Abstract: Human video datasets used for cotraining robot manipulation policies largely consist of curated demonstrations where motions are orchestrated to resemble robot behavior and 3D hand poses are captured with specialized hardware.

Representation Handoffs for OpenArm-Based Laboratory Mobile Manipulation

arXiv:2608. 07154v1 Announce Type: cross Abstract: Open-source robotics and foundation models have lowered the barrier to embodied AI, yet language-guided laboratory automation still requires reliable alignment from instructions and observations to safe actions.

Reachy Mini - The Open-Source Robot for Today's and Tomorrow's AI Builders

Bounding Boxes as Goals: Language-Conditioned Grasping via Neuro-Symbolic Planning

arXiv:2606. 12910v1 Announce Type: cross Abstract: For robotics to be effectively integrated into household or industrial environments, machines must adapt to natural-language prompts in real time.

RoboTok: An Internet-Scale Data Engine for Human Demonstration Retrieval and Dexterous Manipulation Learning

RoboTok is an internet‑scale data engine that retrieves human manipulation videos from the web to train dexterous robot policies. It learns a latent motion space from 3D hand trajectories in actor‑centered reference frames, allowing manipulation behaviors to be compared across different viewpoints, scenes, and occlusions while remaining compact for efficient search. Experiments show RoboTok retrieves more relevant demonstrations and improves downstream robot task success compared to existing retrieval methods.