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Can robots leverage their entire body to sense and interact with their environment, rather than just relying on a centralized camera and end-effector? Introducing RoboPanoptes, a robot system that achieves whole-body dexterity through whole-body vision.
77,202 просмотров • 1 год назад •via X (Twitter)
Комментарии: 15

RoboPanoptes enables novel manipulation capabilities such as unboxing in narrow spaces, sweeping multiple or oversized objects, and stowing in cluttered environments, outperforming baselines in adaptability and efficiency.

Whole-body dexterity allows the robot to utilize its entire body surface for manipulation, e.g., leveraging multiple contact points or navigating constrained spaces. Whole-body vision provides comprehensive observations of its environment and visual feedback of its own state.

Whole-body dexterity and vision is enabled by 1) A modular hardware design that integrates distributed vision and actuation in a scalable and reconfigurable system.

2) An intuitive demonstration interface that streamlines the acquisition of complex whole-body manipulation behaviors.

3) A whole-body visuomotor policy that learns complex manipulation skills directly from human demonstrations, efficiently aggregating information from the camera system while maintaining resilience to sensor failures.

I had a great time working with Dominik Bauer and @SongShuran on this project! Check out our paper: video: and website: for more details!

Super cool work!

Great work Xiaomeng. Great researchers from Stanford often come from Tsinghua University. Thank you for making it open source.

Great thread!

super cool! how complicated/expensive is this to build?

@adv8p The system is low-cost and built with off-the-shelf and 3D printed components! Each camera only costs $6. We will open source all the hardware design and code soon!

With such coverage of the field of view you must have quite a lot of overlapping which could be very useful for 3D reconstruction of the environment.

sweet

@XiaomengXu11 brilliant! I didn't see this in the github page, but how does multi-camera perform on simple grasp task where overhead also sees the object clearly? i assume multi-camera would be slightly better but curious.

Great work! We also addressed whole-body tactile sensing here: Do you think your policy can be applied to our robot for contact-based control?

