u/Biarmrobot

holy sh*t you guys need to check this out: researchers just released open-source designs for 3D printable robot hands with actual tactile sensors built in. it's called "eFlesh" and you can apparently make the sensor skins on a standard FDM or resin printer.

from what i've been reading, these aren't just decorative covers — they're soft optical tactile sensors that give position feedback when you press them. basically like having skin with nerves on your robot gripper instead of steel claws. the full paper and CAD files are in the link above, there's also a complete robot hand design that should be fully printable if you've got the right resin.

i keep thinking about all the little projects i could actually build with this now. like having my Bambu print gripper pads with sensors for maybe $20 in materials instead of buying a whole robotic hand kit. someone in the comments already mentioned they're using it to teach robots object recognition through touch instead of just vision: way more robust when lighting changes or objects are shiny and cameras suck.

anyone else looking at this? i'm tempted to order some soft TPU filament just to test print one and see if the tactile properties hold up. seems like a game-changer for DIY robotics stuff that's usually stuck in academic papers with paywalls.

https://geometryviewer.com/v/bE-fUcenCJlC.stl?cam=3.991%2C1.150%2C5.00&name=eFlesh-DefaultCuboid.stl

https://e-flesh.com/

https://www.tomshardware.com/3d-printing/make-your-own-3d-printable-eflesh-scientists-share-plans-for-affordable-easy-to-produce-and-highly-tactile-robot-sensor-grips

(Have seen confusion about this in other posts, so the quick summary/tl;dr):

The Helix AI models autonomously operate the Figure robot models. (Helix = mind; Figure 01, 02, 03 = body)

Previous videos of Figure robots were autonomously operated by the original Helix model. Figure is now announcing their Helix 02 model, which adds another thinking-system (System 0) as a foundation layer--this new system was trained with human motion data + reinforcement learning.

The Figure 03 robot body has sensors and cameras in its hands, which the Helix 02 model uses for improved dexterity/coordination.

All demonstrations in the video are fully autonomous (not teleoperated).

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Overview from the blogpost:

>Helix 02 is Figure’s most capable humanoid model yet: A single neural system that controls the full body directly from pixels, enabling dexterous, long horizon autonomy across an entire room. Helix 02 represents several breakthroughs:

>-Autonomous, long‑horizon loco-manipulation: Helix 02 unloads and reloads a dishwasher across a full-sized kitchen - a four-minute, end-to-end autonomous task that integrates walking, manipulation, and balance with no resets and no human intervention. We believe this is the longest horizon, most complex task completed autonomously by a humanoid robot to date.

>-All sensors in. All actuators out: Helix 02 connects every onboard sensor - vision, touch, and proprioception - directly to every actuator through a single unified visuomotor neural network.

>-Human-like whole body control from human data: All results are enabled by System 0, a learned whole‑body controller trained on over 1,000 hours of human motion data and sim‑to‑real reinforcement learning. System 0 replaces 109,504 lines of hand‑engineered C++ with a single neural prior for stable, natural motion.

>-New classes of dexterity: With Figure 03’s embedded tactile sensing and palm cameras, Helix 02 performs manipulation that was previously out of reach: extracting individual pills, dispensing precise syringe volumes, and singulating small, irregular objects from clutter despite self‑occlusion.

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Helix 02 Blogpost: https://www.figure.ai/news/helix-02

Announcement on X: https://x.com/Figure_robot/status/2016207013236375661

Hand dexterity video: https://x.com/TheHumanoidHub/status/2016237787067170949

3min Dishwasher unloading video: https://www.youtube.com/watch?v=lQsvTrRTBRs

This is a demo of the MVP I built for a competition.

I’ve been thinking about how robots still struggle to grasp fluid and deformable objects, and I wanted to explore that in Isaac Sim. I noticed there are tools for generating tactile + rigid data or visual + fluid data — but not much for tactile + fluid (please correct me if I’m wrong).

Most of the existing ones are CLI-based, so I thought a UI-driven workflow could make it way simpler and more accessible for newcomers to robot learning.

Let me know what you think.