raunaqmb
Joined 2 years ago
Karma
181
Submissions
19
Member since
Sep 19
roboticist
View on Hacker NewsRecent submissions
Comment · 1 year ago
That's a great question! You probably need 2x the thickness of the magnets just so there's some amount of deformation possible. Could you clarify which modeling you are talking about for the deformation estimation?
Custom geometries will require a new network, yes!
Comment · 2 years ago
The fun thing about using microparticles is that there's no dead zone! In fact, the edge response is even stronger (as you can see on the video on our website) because despite the distance from the chips, the skin is much more deformable at the edges.
Comment · 2 years ago
We only leave the circuit design out because it is identical to Reskin! https://ReSkin.dev
More than happy to answer questions about it either here or on my email as the corresponding author on the paper!
Comment · 2 years ago
You could, and this is what we did with ReSkin, https://ReSkin.dev
The reason we don't want to do this is that it is difficult to cover all possible characteristics. Say we do single point contact localization, and 3-axis forces prediction. What happens when we have multi-point contact? The calibration has only been used to calibrate/align in a lower dimensional space. This is primarily why not needing calibration and baking this into the hardware is a lot more appealing. The user/designer no longer needs to think about the task and the dimensions of alignment required for that task.
Comment · 2 years ago
While the sensor gives us direction vectors, they serve as good proxies for contact location, as we showed with ReSkin, https://reskin.dev.
That being said, the exact quantities the policy depends on are hard to interpret, given the use of deep learning. This could potentially be modality agnostic, but there has been no sensor so far that has shown (1) the ability to detect intuitively relevant quantities like contact location and 3-axis forces, and (2) sufficient signal consistency for deep learning models to generalize across instances. This was a key motivating factor for AnySkin, and we found a relatively straightforward fabrication procedure that enables this for magnetic sensing.
Comment · 2 years ago
Exactly! You need little to no re-calibration.
With capacitative sensors, it is unclear from existing literature if it is possible to detect shear. Additionally, they generally operate at significantly lower frequencies.
Comment · 2 years ago
While this is possible, it would create stress concentrations within the elastomer and could significantly affect its durability. We saw this effect even when using larger magnetic particles as with ReSkin, https://reskin.dev. If instead we make the elastomer more rigid, it would worsen grasp stability.
Comment · 2 years ago
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.
Comment · 2 years ago
As for what it is sensing, we learn end to end policies in this case, and allow the neural network can pick up on whatever it needs for the particular task! but we have run experiments with a predecessor of AnySkin, ReSkin: https://reskin.dev that indicate you can localize contact at sub-mm scale as well as sense normal and shear forces!
Comment · 2 years ago
We are just collecting emails on the Google form as contact information to get more details when shipping samples. I am sorry that the form is asking for a google account - we will fix that as soon as possible.
Comment · 2 years ago
Yes, and importantly we find that visuotactile policies work even when replacing skins. This hasn't been shown before, to the best of our knowledge, and opens the door to a number of exciting large-scale applications of this sensor.
Comment · 2 years ago
We use the MLX90393
Comment · 2 years ago
Yes, you can 3D print a mold and we release this design tool: https://cad.onshape.com/documents/f3ec62110b01a3ad0fcb6d85/w...
You can make whatever 2D shape you want in shape_sketch, as long as it is within the bounding square, and we automatically generate molds with the requisite inlet and outlet channels! It is still in prototype mode and we are working to make it robust, but it generally works and was used to make all the different shapes you see on the website and in the paper.