Kindly Robotics Blog
Updates on robotics for good, our mission, and what we're building — from disaster response and community cleanup to the future of physical AI.
An Open Episode Interchange Format for Surgical Physical AI
Surgical AI has excellent per-task datasets but no shared episode interchange format — no equivalent of what Open X-Embodiment/RLDS, LeRobot, and DROID became for robot learning. We propose one: a four-layer episode schema that serializes to RLDS/LeRobot, ships with an installable validator, and is demonstrated on real public labels (CholecT50/Cholec80). It's a v0.1 proposal inviting critique — not an adopted standard.
Sim Isn't Enough: What the NVIDIA × NHS × Apian Story Tells Us About Clinical Physical AI
Apian's photorealistic NHS digital-twin partnership with NVIDIA — built on Project Rheo / Isaac for Healthcare — validates that hospital physical AI is now a real-money category. It also surfaces the layer simulation can't generate: credentialed, real-world clinical demonstration data. A clear-eyed analysis written pre-IRB, pre-OR-capture, pre-customer.
Why Egocentric Warehouse Data Won't Train a Surgical Robot
Mecka's EgoVerse, Build AI's Egocentric-1M, and Lightwheel's EgoSuite are racing on hour-count. For surgical robotics specifically, the implicit transfer assumption breaks across five concrete mechanisms — manipulation kinematics, viewpoint stability, tissue physics, failure-cost-shaped action distributions, and task hierarchy. A clear-eyed technical analysis of the domain gap, written pre-IRB, pre-OR-capture, pre-customer.
What HIPAA-Compliant Robot Training Data Actually Looks Like
A public design document for an IRB-cleared, HIPAA-aligned surgical data capture protocol — what it requires at the consent, pipeline, and credentialing layers, and why the funded egocentric data vendors can't ship it. Written pre-IRB, pre-contract, pre-OR-capture.
Mecka AI Alternatives: A Map of the Physical AI Data Engine Category
A neutral, vendor-by-vendor map of the physical-AI data-engine category in 2026 — egocentric collection, teleop-as-a-service, and platform/annotation plays — with verified funding, named customers, and the questions buyers should ask before signing.
No Lock-In: A Round Trip Between LeRobotDataset v3 and RLDS with FoodforThought
A practical guide to moving robot-demonstration datasets between formats: importing Hugging Face LeRobotDataset v3 and Open X-Embodiment (RLDS) into FoodforThought, and exporting labeled recordings back out to LeRobotDataset v3 and RLDS — the open standards the robot-learning ecosystem actually trains on.
A Practical Guide to Action-Segmentation Labeling for Robot-Demonstration Video
How to label robot-demonstration video accurately: frame-exact in/out points, overlap and gap rules, temporal-IoU consensus QC, and exporting clean segments to LeRobotDataset v3 and RLDS. Includes a 2-minute hands-on try-flow.