| This is a very insightful summary, thank you! A few things to add about AnySkin that might be relevant: - AnySkin expressly handles wear and gunk by being replaceable. So if it wears out, and you have a heuristic or learned model for the old skin, it will work pretty well on the new skin! We verify this through an analysis of the raw signal consistency across skins, as well as through visuotactile policies learned using behavior cloning. We found swapping skins to work for some pretty precise tasks like inserting USBs and swiping credit cards. - Could definitely be used for part motion detection - Soft, inflatable grippers are effective, but often passive. AnySkin is not just soft, but also offers contact information from the interaction to actively ensure that blueberry doesn't get squished! - This sensor would be key for robots that seek to use learned ML policies in cluttered environments. Robots are very likely to encounter scenarios where they see an object they must interact with, but the object is occluded either by their own end-effector(s) or by other objects. Touch, and an understanding of touch in relation to vision becomes critical to manipulate objects in these settings. - Industrial robots do have very sensitive motor and arm feedback. However, these systems are bulky and unsafe to integrate into household robotic technologies. Sensors like AnySkin could be used as a powerful, lightweight solution in these scenarios, potentially by integrating with some exciting recent household robotics models like Robot Utility Models. - ReSkin, the predecessor to AnySkin, has previously been used quite effectively for fabric manipulation! (see work from David Held's group at CMU). AnySkin is more reliable as well as more consistent and could potentially improve the performance seen in prior work. |
I bet having good touch sense would let you get away with much cheaper mechanical systems for the robots.