5,000 Hours of Touch, and Four Ways to Get It - by Jay Chia

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5,000 Hours of Touch, and Four Ways to Get It<br>Tactile is the modality everyone agreed was missing from robot pretraining at scale. 5 groups went after it in the past week, with similar methods but different answers on where the data comes from.<br>Jay Chia<br>Jul 30, 2026

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Four routes to tactile data

OpenNeoData is the largest of them. NeoteAI and Fudan released 200,000+ trajectories, more than 5,000 hours across six embodiments, with two gel visuotactile cameras on every gripper time-aligned to RGB at 30fps for the entire dataset, in LeRobot v3.0 format. They shipped two models trained on it, N₀-VTLA, which they describe as the first VLA pretrained on tactile data at scale, and N₀-TWAM, a world-action model that predicts future contact alongside future vision. Being-H0.8 avoids the sensor cost at pretraining time, using a model called TactoHand to infer dense contact and proximity fields from over 500,000 hours of ordinary egocentric human video, then using measured pressure from instrumented hands where it has it. ViTacWorld leans on simulation, and makes the interesting argument that tactile signal may cross the sim-to-real gap better than vision does, because contact is grounded in physics rather than appearance. τ is the cheapest, learning a tactile representation from future visual supervision in latent space during training only, so it adds nothing at inference. FeelWorld predicts contact, force and slip as separate states, gating them so free-space motion does not degrade the visual prediction.<br>What is striking is how much they agree on once the data is in hand. Four of the five train on predicting future tactile state. N₀-TWAM predicts future vision and contact together, ViTacWorld predicts both from an action sequence, FeelWorld predicts visual latents alongside contact, force and slip, and Being-H0.8 learns from future visual and tactile evidence. The representations differ underneath, N₀-TWAM using a force-based encoding and Being-H0.8 contact fields over a hand mesh, but the objective has the same shape. The disagreement sits upstream of the model, over what an hour of contact data should cost.<br>Which leaves the question nobody has answered. Being-H0.8’s pseudo-labels cost almost nothing per hour and cover a corpus a hundred times larger than OpenNeoData’s, while OpenNeoData’s are real measurements from real sensors. Only one side of that comparison is downloadable. OpenNeoData is up, gated behind a contact form; UniHand 3.0 is not, and neither are Being-H0.8’s weights.<br>Thanks for reading Topic Queue! Subscribe for free to receive new posts and support my work.

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Research

FLUX 3 drives robots on Audi’s floor

Black Forest Labs released FLUX 3, a multimodal model trained on tens of millions of hours of video, and gave mimic robotics early access to build FLUX-mimic. A compact action decoder reads intermediate features from the video-prediction path and denoises action chunks straight out of them, so no video is rendered at inference and a chunk costs one forward pass. On a soft-body kitting task on real hardware they report 95% success with no task-specific fine-tuning, against 55% for an adapted π0.5 trained on the same data mix, and full-system reaction time of 101ms on an RTX 5090. The numbers are mimic’s own on their own tasks, FLUX 3 is early access, and the open-weight backbone is promised rather than out.

HiFi-UMI removes the real-robot anchor

The usual recipe is to scale with robot-free data and then anchor with a little real-robot teleoperation. HiFi-UMI asks whether the anchor is a consequence of low-fidelity capture, and builds a rig for 3mm end-effector accuracy with under 40µs cross-sensor sync and roughly 200° of coverage per gripper. A policy post-trained only on their data deploys directly and matches in-domain teleoperation on three separate backbones, within 3.1 percentage points on both VLAs tested. They released HiFi-UMI-2K, a 2,000-hour curated slice of a 20,000-hour corpus, with every episode replayed in simulation and quality-checked.

πR² makes flow policies react mid-chunk

Action-chunking flow policies run open-loop inside a chunk, so they cannot respond to anything that arrives mid-execution, and the backbone is too slow to simply replan more often. πR², from CMU, splits conditioning into a fast proprioception channel refreshed every tick and a slow vision-language channel updated asynchronously, then emits actions in one denoising step per call. Applied to GR00T-N1.7 on a real xArm6, it replans roughly 4x faster than the base policy at 25Hz, acting on a fresh observation every 40ms, and improves real-world success by up to 30%. It finetunes from an existing checkpoint with minimal architectural change, and one trained model adapts to whatever hardware latency you have.<br>A scaling law for precision, and a way to move it

The Curse of Precision, from Tsinghua, measures how...