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- Hacker News
- Congrats on the launch. "Successfully executing a trajectory doesn't mean the inspection worked" is a great frame, closing the loop on the measurement itself is the hard part.by akshay_akula
- Makes sense, catching a bad measurement in the moment beats finding out after the robot has left the site.by akshay_akula
- Thanks, analyzing the data collection in real time plays a major part in assuring we actually collect the data we set out to.
- Congrats on launching. This should get more attentionby sidcool
- Thanks, YC launches have helped quite a bit with drawing attention. We're hoping HN will pronounce it more.by janakpanthi
- Congrats on the launch! At Transitive Robotics we make full-stack modules so robotics companies like you can build their operational systems faster and with greater quality. We have a number of modules you might be interested in, e.g., for remote video-streaming and tele-op/-assist: https://transitiverobotics.com/caps/. Let me know if you want to chat.by chfritz
- Remote video streaming is relevant. Please free to send us an email.by janakpanthi
- Is "Salem" a tongue-in-cheek nod to being a small, quirky competitor to Boston Dynamics (Salem, MA : Boston, MA :: Salem Robotics : Boston Dynamics)?
If so, as someone who lives close to Salem, I like it :)
Good luck to you guys.
by droidjj - Salem Staticsby sidcool
- First thing I thought of too, I love itby alexchantavy
- Haha it wasn't, but we might steal that for our story going forward.
- All the best! Very clear framing and definitely needed service. I remember in 2011 not too far from Fukushima after the problems with nuclear reactors. The company here faced very difficult inspection problems, including through rubbles. There were robots able to deal with some problems, but not in Japan, and anyway they had to act fast and so sent people. It apparently was safe enough, but able robots would have been so much safer.
A technical question: To scan a surface precisely, I'd rather put switches on the end effector aside the sensor, such that the arm places itself roughly above the surface to scan, pushes until the switches trigger, and scan keeping the switches on. A simple touch sense, basically. All trad robotics that smoothly follows many flat and curved surfaces. Not at all against AI here, it seems the whole thread is a constructive approach mixing the best tools to concrete targets. Not viable approach in your scenarios?
Thoughts on your concluding questions:
> where the abstraction boundary in robotics should sit. What should come from the robot manufacturer, what belongs in an application layer, and what will inevitably remain specific to the facility?
I really like when there is a "double SDK", with a low level one to target actuators and sensors individually (motor 2 of leg 4), and a higher level one with pre-defined scenarios (move forward, whatever the bot is a bipede, a quadcopter or a slug). These two API types often allow for easier work at the application layer. The SDK is also the main specific software that remains with the maker (facility?)
> industries where you've seen physical inspection tasks that look trivial to a person but are surprisingly difficult to automate
Too often a problem I have seen is... elevators. More generally unstructured environment, and environments explicitly tailored for humans. Proposals range from wireless interfaces (and so HW infrastructure dependent and hard to scale), to adding a manipulator to, say, push buttons on an elevator (and this means extra software to manipulate, recognise buttons, etc).
by hic - Congrats on the launch. I've been in the robotics industry for a decade now, I've seen that boundary lie at many different places. I'm not sure there's a clear cut answer.
Self plug: I'm the founder of Clipper, a container registry that has 10x faster pulls and 7x faster builds over DockerHub, targeted at robotics. I got tired of robotics deploys taking all day and fixed it myself. If you're interested in that or just want to talk shop, let's chat.
by a_t48 - What about container registries do robotics companies need that are different from other industries?by ryanisnan
Congrats on the launch. I love this part, because it is also the working theory behind my robot clearing robot project (http://www.frost-e.com). I'm new to robotics, but it seems to me there's a gap between classic robotics tools and approaches (ROS2-based navigation, local YOLO-based object detection etc) and the video-input-to-actuator-output VLA models that robot labs are showcasing. In fact, I kinda think the big labs are trying to boil the ocean - long horizons, 6DoF actuator control, cross embodiment, generalization, etc etc.> We use AI where semantic understanding and flexibility are useful, such as interpreting less structured information or understanding what in an unfamiliar scene is relevant to a procedure. Once the system knows what physical interaction it needs to perform, we prefer explicit geometry, planning, optimization, and control where possible. We're interested in the marriage between the two rather than trying to make every part of the robotics stack learned.Seems to me is that if you restrict the problem space, have well defined tasks, then marry the semantic understanding and reasoning of VLMs with the geometric tools of classical approaches, then you could have something practical and economical.
- Yeah, you hit some great points! From a broader perspective not only are we seeing the sort of ai vs classical approaches camps at different conferences and companies, but industry wide we wonder about another shift.
A shift from a single company handling all software and hardware inhouse, to a sort of divergence of the two. Kind of cool to think about, given how that sort of divergence seems to have happened in a few other areas (a dell computer runs windows, an LG TV runs apple TV, your iphone runs airbnb, spotify, instagram)
- Great point and realisation on many realities of robotics. Most successful and scaling projects have been traditionally in controlled environments. This always reminds me of William Shatner in "The Future is Now" ad in 1984: https://www.youtube.com/watch?v=pb_-BWHMQlc
When we can control the environment precisely, we should as it simplifies so much---we can clip the world to platonic geometries and apply common tricks and trigonometry.
But when we cannot control the environment, really really much harder. Self-driving cars are a good example how hard it is, and still mainly focusing on relatively structured environments like cities.
Arbitrary environment is really where many expect AI/ML/probabilistic robotics to bring some necessary flexibility. And still out of reach today, except some astonishing use cases like Roombas (well, more like a best effort approach, still).
by hic - The part about being hardware-agnostic sounds something like ROS 2, but maybe at a higher level. I would love to see a post about that (with a bit of explainers for people like me who only lightly dabble with robotics).
But I'm also curious to know if the "Salem" name was searched first. When I first saw the title I thought it was talking about Agility Robotics, which is based in Salem, Oregon and is currently 3rd spot on Google search for "Salem Robotics" (and 4 of the top 5 results on YouTube).
by beckford - > We use AI where semantic understanding and flexibility are useful, such as interpreting less structured information or understanding what in an unfamiliar scene is relevant to a procedure. Once the system knows what physical interaction it needs to perform, we prefer explicit geometry, planning, optimization, and control where possible...
> A few centimeters of error may not matter much when navigating down a hallway, but it matters if a sensor is supposed to remain normal to a curved surface.
This is a great specific example of where the creativity (read: randomness) of AI runs into a wall. Do you see this changing over time as models improve, or do you expect that safety-critical / highly specific tasks will always require a more explicit set of instructions?
by lesiva - That creativity/randomness presents itself in different ways depending on the application of the AI. In robotics, that randomness means variance in the motion of the robots physical body and arm(s). I think over time as labs get better at understanding and quantifying the uncertainty of model outputs there could absolutely be a place for full end-to-end AI in safety-critical environments. But today, we believe that is not the best approach for these applications.