Comment by martythemaniak
11 hours ago
> 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.
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.
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.
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).
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)