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trilogic 2 hours ago [-]
This is brilliant, the very future of humanity and huge market share for the next 30 years. The cards on the table too early can be a mistake. With Qwen background this can be mass production like 1 Million units/year in the next 3 years. Think of excavators but in minisize for human use. OMG Europe look at this and take note, every industry dream, the robot suit. It will take over the car market by X10 fold in the next decade. Please Europe get on this fast.
aliljet 2 hours ago [-]
This sounds incredible. Have these models effectively solved the problem of trying to use a fast-processing network to predict the world's state ahead? For example, to catch a ball?
w10-1 2 hours ago [-]
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.
martythemaniak 2 hours ago [-]
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.
toephu2 16 minutes ago [-]
Is it open source?
Kuyawa 55 minutes ago [-]
Think, move, sense, at a neck breaking pace. Awareness is just an iteration away.
lukewarm707 5 hours ago [-]
qwen just keeps delivering, it's too good
amluto 4 hours ago [-]
The qwen.ai webpage should learn to deliver plain HTML with its content instead of overcomplicated JavaScript and CSS to display obnoxious pulsating rectangles where the content ought to be but hasn’t managed to load.
halJordan 3 hours ago [-]
Bike shedding exhibit a:
amluto 2 hours ago [-]
I don’t think it’s bike shedding if I literally cannot load the content. This is a recurring problem on these qwen.ai announcements.
wiremine 4 hours ago [-]
Nice! What are some hardware platforms that leverage these models?
weberer 3 hours ago [-]
The site says they're running them on a NVIDIA Jetson Thor, which dev kits start at $3,000.
agilob 4 hours ago [-]
I'm pretty sure we're going to see HASS and valetudo using it soon, fingers crossed
idiotsecant 2 hours ago [-]
I can't view the videos on my phone. How much existential terror should I feel?
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.
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.