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Can we bring human-like Touch to robots🤖? Introducing our CoRL work on 3D-ViTac. Humans rely on both vision 👁️ and touch 🫳 for complex tasks. With combined visual-tactile sensing, robots can now tackle challenging tasks, like precise in-hand reorientation, fragile objects grasping. Website: #Robotics #CoRL2024 #Touch #tactile #AI #ML

49,544 Aufrufe • vor 1 Jahr •via X (Twitter)

10 Kommentare

Profilbild von Binghao Huang
Binghao Huangvor 1 Jahr

An open-source tactile sensing system from @Columbia designed to democratize touch to robot data collection. Please check out our website for tutorials ( The sensor is equipped with dense sensing units, each covering an area of 3 millimeter square. These sensors are low-cost and flexible, providing detailed and extensive coverage of physical contacts, effectively complementing visual information. (2/9)

Profilbild von Binghao Huang
Binghao Huangvor 1 Jahr

We install flexible tactile sensors on soft gripper to get touch information and use multi-view RGBD cameras to get visual information. To integrate tactile and visual data, we fuse them into a unified 3D representation space that preserves their 3D structures and spatial relationships.(3/9)

Profilbild von Binghao Huang
Binghao Huangvor 1 Jahr

With 3D-ViTac, robots can now handle delicate objects, like eggs, with ease! 🥚 In this Egg Steaming task, the robot first uses its right hand to open the egg tray, then carefully grasps and places an egg into a egg cooker (narrow space). (4/9)

Profilbild von Binghao Huang
Binghao Huangvor 1 Jahr

The robot keeps retrying until it successfully grasps the grapes even under huge visual occlusions. (5/9)

Profilbild von Binghao Huang
Binghao Huangvor 1 Jahr

We observe three key benefits of integrating touch. (1) Tactile sensors provide critical feedback on the presence of contact and the appropriate amount of force to apply. (2) Our policy leverages detailed contact patterns provided by touch to address visual occlusions effectively. (6/9)

Profilbild von Binghao Huang
Binghao Huangvor 1 Jahr

This project is led by @binghao_huang and would have been impossible without the hard work from co-authors: @YXWangBot , Xinyi Yang, @LuoYiyue , @YunzhuLiYZ. (7/9)

Profilbild von Tony Kam
Tony Kamvor 1 Jahr

Awesome!

Profilbild von Jason
Jasonvor 1 Jahr

Nice work. I’ve seen other tactile sensing work emphasize improvements picking and placing deformable objects. But this is the first one I’ve seen that emphasizes visual occlusion performance. The grape demo is really cool! Are there plans for your lab to continue iterating on the hardware for the tactile sensing system?

Profilbild von Binghao Huang
Binghao Huangvor 1 Jahr

Thanks, Jason! Yes, we’re continuing to iterate on our hardware. Over the next few weeks, we'll release an easy-to-follow guide for reproducing our flexible sensor. In future iterations, we're planning to develop flexible PCBs to produce sensors directly from the factory, allowing for easy scaling.

Profilbild von Stormcatch ⚡️ ($SCATCH)
Stormcatch ⚡️ ($SCATCH)vor 1 Jahr

Yes the human-like touch will come!

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I was really impressed by the UMI gripper (Cheng Chi et al.), but a key limitation is that **force-related data wasn’t captured**: humans feel haptic feedback through the mechanical springs, but the robot couldn’t leverage that info, limiting the data’s value for fine-grained manipulation tasks. Led by my amazing students Yolanda Zhu and Binghao Huang, we designed a **portable visuo-tactile gripper** by integrating our dense, flexible tactile arrays with the UMI gripper to enable large-scale in-the-wild data collection. 🔗 We demonstrate **cross-modal representation learning** and **downstream policy learning** on tasks requiring in-hand state estimation (e.g., test tube reorientation) and fine-grained force sensing (e.g., pipette fluid transfer). Key takeaways: - Our flexible tactile arrays store the rich haptic information humans perceive as dense tactile signals. - Portability and robustness are key for in-the-wild data collection; our portable gripper is compact, lightweight, and durable. - Touch provides precise, robust measurements of in-hand object pose, invariant to lighting and viewpoint. - Cross-modal pretraining on large-scale in-the-wild data significantly improves policy robustness and sample efficiency (as shown many times before — and verified again here!). Also check out our previous investigations of dense, flexible tactile grids for understanding human-robot-environment interactions: - Dense tactile glove (Nature ’19): - 3D-ViTac (CoRL ’24):

Yunzhu Li

13,188 Aufrufe • vor 1 Jahr

A policy that teaches robot hands to touch things the way humans do... not just grab and move, but feel and adjust in real time. Robot manipulation research often stops at picking up objects and placing them. CGP goes further: it handles tasks like opening jars, flipping objects in-hand, wiping dishes, and grasping fragile eggs, the kind of dexterous, contact-rich skills that require constant micro-adjustments based on what the fingers are actually feeling. The robot doesn't just see what it's doing; it predicts what contact should feel like at each step, then checks whether reality matches the prediction. If a finger is slipping, the policy knows before the object drops. Works on real robot hands (both 4-finger and 5-finger designs) with tactile sensors embedded in the fingertips Robust to visual distractions! The robot keeps flipping a box correctly even when the camera view is disrupted, because it's grounding decisions in touch, not just vision. Baseline policies without contact grounding fail in predictable ways: slipping mid-task, incomplete motions, loss of grasp, CGP avoids these This is a meaningful step toward robots that can handle the physical world with the kind of reliable, adaptive grip that humans take for granted. Relevant for manufacturing, logistics, assistive robotics, and anywhere fragile or irregular objects need to be handled carefully. Published at RSS 2026, developed with Meta Reality Labs Research. Thanks for sharing, Zhengtong Xu / Zhengtong Xu ——- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

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