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Learning Time-Optimal and Speed-Adjustable Tactile In-Hand Manipulation by DLR on humanoids 2024

21,319 просмотров • 1 год назад •via X (Twitter)

Комментарии: 3

Фото профиля Michael Cho - Rbt/Acc
Michael Cho - Rbt/Acc1 год назад

Really impressive stuff!

Фото профиля RICE AI ( ◉ - ◉ )
RICE AI ( ◉ - ◉ )1 год назад

Amazing control

Фото профиля darthur
darthur1 год назад

Oh, come on! Orient the letter properly.

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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 просмотров • 1 год назад