Combining the robot operating system with LLMs for natural-language control
Hi there, little explorer! 👋
Imagine you have a super-duper toy robot 🤖. Right now, you might have to push buttons to make it move, right?
This news is about making robots even smarter! It's like giving your robot a special brain 🧠 that can understand your words!
So, instead of pushing buttons, you could just tell your robot, "Hey, robot, please pick up that red block!" And it would know what to do! ✨
It's like magic, but it's really clever computers helping robots understand us better, just like you understand your mommy or daddy when they talk to you! Isn't that cool?
Over the past few decades, robotics researchers have developed a wide range of increasingly advanced robots that can autonomously complete various real-world tasks. To be successfully deployed in real-world settings, such as in public spaces, homes and office environments, these robots should be able to make sense of instructions provided by human users and adapt their actions accordingly.
Could not retrieve the full article text.
Read on Phys.org AI →Sign in to highlight and annotate this article
Conversation starters
Daily AI Digest
Get the top 5 AI stories delivered to your inbox every morning.
More about
autonomousresearch
Salt: Self-Consistent Distribution Matching with Cache-Aware Training for Fast Video Generation
Video generation models are distilled using self-consistent distribution matching to improve quality under extreme inference constraints, with cache-aware training enhancing real-time autoregressive generation. (1 upvotes on HuggingFace)
Swift-SVD: Theoretical Optimality Meets Practical Efficiency in Low-Rank LLM Compression
Swift-SVD is a compression framework that achieves optimal low-rank approximations for large language models through efficient covariance aggregation and eigenvalue decomposition, enabling faster and more accurate model compression. (3 upvotes on HuggingFace)
Knowledge Map
Connected Articles — Knowledge Graph
This article is connected to other articles through shared AI topics and tags.
More in Models
Anyone got Gemma 4 26B-A4B running on VLLM?
If yes, which quantized model are you using abe what’s your vllm serve command? I’ve been struggling getting that model up and running on my dgx spark gb10. I tried the intel int4 quant for the 31B and it seems to be working well but way too slow. Anyone have any luck with the 26B? submitted by /u/toughcentaur9018 [link] [comments]
Discussion
Sign in to join the discussion
No comments yet — be the first to share your thoughts!