6/16/2026 at 7:39:10 PM
Was it expected that Qwen is working on this? What are the current alternatives?The TAM for robots is much, much larger than for coding or services, and much more strategic when you think about manufacturing and war-making.
The Qwen "suite" is a workmanlike breakdown with demonstrated tasks that seems to me as an outsider to suggest that one could start building integrated systems this year, and have simple products next year. I'd be very interested in an assessment from engineers from the robotics companies (cars, biomedical robots, manufacturing...).
Elsewhere on HN I see hundreds of comments on SpaceX's long-telegraphed merger with Cursor but no serious evaluation of this.
by w10-1
6/16/2026 at 8:11:09 PM
I come from a regular swe background, but I've spent the last few months getting into robotics and trying to build a snow-clearing robot, so here's my noob notes:First, very much expected. Both Google and Qwen have been building explicit spatial reasoning and spatial output capabilities in their models since last fall, gemini 3 was released with support for outputting trajectories for example. I only took a look at Robonav (more relevant for my needs) and its architecture and capabilities are inline with other similar models (eg nVidia's alpamayo).
Second, the overall architecture they describe mirrors what I've been working on: You have general purpose LLM that takes a look at the works and the task in front of it and reasons to break it down into subtasks and tool calls, and you can think of RoboNav and RoboManip as tool calls here. The harness keeps a memory and manages the context of the LLM and tools and keep looping until the objective is complete.
Consider the task of clearing snow off a driveway using this suite: An LLM (Qwen 3.7 plus) takes look at the driveway and decides which areas to clear. The harness then tells robotnav to go to an certain location, then robotnav takes over an runs in a loop until the robot is that that location. Then the harness tells robotmanip to use the plow to clear strip of snow. The harness will then call the planner LLM to plan an execute the next clearing and repeats until the driveway is clear.
So what' the issues? Well, they didn't release the weights, nor the training scripts so you can't actually use it. But also, it's all very research-y still, the models are "small" but still huge/expensive for current edge hardware. You'd still need lots of data collection, HITL, and fine-tuning and evals to make it work for your task. You'd also need a secondary safety system to make sure the models don't wreck something. But overall, I do expect robots to use an agent/model combo like this in prod in a few years.
by martythemaniak
6/16/2026 at 11:43:00 PM
This is bananas to me. Theres been successful entries to snow plow competitions for ages. What a world that people now expect networks to handhold through it. Irresistable to all parties I suppose.Well I guess I'll have to have a look!
by jvanderbot
6/17/2026 at 2:44:38 PM
Yeah, there's commercially available snow plow robots, you can buy a Yarbo for your house today. As far as I can tell, they all operate on a classical robotics stack - for the Yarbo you install an RTK antenna to give the robot cm-level precision, define a map and a routine, then the Yarbo can execute that routine by itself.But can it deal with arbitrary lots without extensive premapping, manage piles, handle obstacles intelligently, correct itself (ie spot needs a second clearing ), tackle windrows, etc? It can't, and my hunch is that LLMs are the first tech we have that can plausibly handle all the various cases that a proper robot would need to handle.
by martythemaniak
6/17/2026 at 6:47:10 PM
My hunch is that some kind of planning stack with environmental awareness at a network level is a good solution to this. My hunch is that LLMs aren't really it. Maybe VLA but I'd bet lower.Robotics probably will absorb a lot of Rl/diffusion-based tech, with LLM at a high level interface at best.
by jvanderbot
6/17/2026 at 8:13:50 PM
Yeah, afaik the approach people take today is always some form of bi or tri level hierarchical control, with a slow LLM doing planning and sub task management and diffusion or VLA doing the motor control at higher frequencies. Major differences seem like where and how you draw the boundaries. For my project I'm personally trying to use ROS2 as a low level tool call (instead of diffusion), with an agent /LLM doing the main decisions.Having said that, this scheme seems like it might just be a reaction to current hardware limitations. When I saw Talaas demonstrate a 8B model running on a custom chip at 17k Tok/sec, first thing I thought was "wow, you can just run an LLM in a control loop"
by martythemaniak
6/17/2026 at 6:59:52 AM
> This is bananas to me. Theres been successful entries to snow plow competitions for ages.Why do you hate subscriptions? What if you get a summertime snow storm?
by officialchicken
6/17/2026 at 9:38:57 PM
I wonder if there is hope for clearing ice off asphalt and concrete. It's a real problem in Scando, where temps can hover around freezing longtime, for repeated thaw/freeze cycles.by euroderf
6/17/2026 at 5:01:38 AM
> The TAM for robots is much, much larger than for coding or serviceshow do you figure?
by rsalus
6/17/2026 at 6:00:44 AM
The physical work in the world far outstrips the information work. Most information work is simply organizing physical work, attempting to make physical work more efficient.by Schiendelman
6/17/2026 at 6:18:36 AM
The promise of intelligence might be larger still. By scaling and using superintelligent LLMs to write code for itself, it's possible that the whole field of robotics is just another problem you can point LLM agents at and expect to be solved by afternoon, just like one of those math puzzles. "Traditional" robotics R&D (or any R&D really) would be worthless due to abundance.by crazylogger
6/17/2026 at 9:22:45 AM
You're just confirming GP's point. If AI agents make those software problems trivial, the physical tasks are all that's left.Regard it as market segments. It's not hard to envision eg. agriculture & food processing robotized to the point where no human ever touches your food. A few generations in, and people would see potatoes as "nutrient-containing object that comes from a factory" and forgot how to grow potatoes.
I'm rooting for the 'market segment' where AGI (or ASI) finds solutions to long-standing science questions, that are hard to obtain but easy to verify. Or makes new discoveries. Stuff like cancer research, protein folding, synthetic biology, new materials, battery tech, number theory, particle physics, etc etc.
by RetroTechie
6/17/2026 at 10:17:25 AM
I'm with you. As these models grow in ability and commoditize across the TAM of basically every business in the world, it's going to get cheaper and cheaper to solve everything.by Schiendelman
6/17/2026 at 6:01:43 AM
How many people have homes that require chores to be done? Laundry, cleaning, setting/clearing tables, yard work, some consider cooking a chore.If I could get an affordable robot to do a subset of them, I'm in the market for one.
by ragebol
6/17/2026 at 1:06:58 PM
Makes complete sense! In fact, it seems to me that the most value sits in those messy, unstructured environments like cluttered homes.I wonder... How can these foundational models actually learn to deal with that without being deployed in those scenarios? It feels like a chicken-and-egg problem: you need a ton of real-world data from chaotic homes to train robust models, but you also need robust models before you can safely deploy robots into those exact homes at scale
by aamdias