Sumeet Singh @Sumeet_Robotics
Research scientist at Google Brain Robotics in NYC. All views are my own. web.stanford.edu/~ssingh19/inde… New York, NY Joined August 2018-
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Sumeet Singh retweeted
Meet Gemini Robotics: our latest AI models designed for a new generation of helpful robots. 🤖 Based on Gemini 2.0, they bring capabilities such as better reasoning, interactivity, dexterity and generalization into the physical world. 🧵 goo.gle/gemini2-roboti…
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A well reasoned and considered response.
Instead of blindly trusting an LLM to plan a robot's next step, see how to use Conformal Prediction to ground its uncertainty and ask for help when needed. 👇
Instead of blindly trusting an LLM to plan a robot's next step, see how to use Conformal Prediction to ground its uncertainty and ask for help when needed. 👇
Sumeet Singh retweeted
Presenting our robotic table wiping work today at 3pm, Pod 15 at #ICRA2023 ➡️ Come have a chat about reinforcement learning, trajectory optimization, and stochastic dynamics modeling
Presenting our robotic table wiping work today at 3pm, Pod 15 at #ICRA2023 ➡️ Come have a chat about reinforcement learning, trajectory optimization, and stochastic dynamics modeling
Read about Thomas' internship last summer!
Sumeet Singh retweeted
A summary of Google Robotics in 2022, including our work on novel trajectory optimizers and applications!
Sumeet Singh retweeted
Glad to see our Performer-MPC, a 8.3M-parameter Transformer model with <10ms on-robot latency paired with Model Predictive Control, is mentioned by this Robotics@Google blog! ai.googleblog.com/2023/02/google… Check out our paper at cs.gmu.edu/~xiao/papers/p… @kchorolab @xf1280 @vikassindhwani
Sumeet Singh retweeted
Great article on LLMs - MLE training is exactly lossy data compression! I know RLHF is changing the loss to maximize predicted human ratings, but it still feels like a bit like lipstick on a pig to me (although who doesnt like bbq now and then? ;) newyorker.com/tech/annals-of…
Check out the table-wiping robot from Thomas' internship! Great example of combining the strengths of RL and classical trajectory optimization for modularity, generalization, and efficient learning. Disclaimer: surface cleaner sold separately 🙃
Check out the table-wiping robot from Thomas' internship! Great example of combining the strengths of RL and classical trajectory optimization for modularity, generalization, and efficient learning. Disclaimer: surface cleaner sold separately 🙃
Sumeet Singh retweeted
I'm excited to present Single-Level Differentiable Contact Simulation. It's a novel formulation that unifies contact dynamics and collision detection in a single optimization problem. paper: arxiv.org/abs/2212.06764 code: github.com/simon-lc/Silic… video: youtu.be/oaGLTR13iF8
Sumeet Singh retweeted
Work from our @GoogleAI team - training robots to play table tennis to better understand how they learn in dynamic + high-speed settings. One project achieved a 300+ hit rally, while another is focused on matching the precision of amateur human players 🏓 ai.googleblog.com/2022/10/table-…
Come check out this workshop on Monday at #RSS2022! I'll be presenting (poster + spotlight) our latest work on multiscale sensor fusion architectures for continuous-time control using Neural CDEs.
Come check out this workshop on Monday at #RSS2022! I'll be presenting (poster + spotlight) our latest work on multiscale sensor fusion architectures for continuous-time control using Neural CDEs.
Sumeet Singh retweeted
Super excited to introduce SayCan (say-can.github.io): 1st publication of a large effort we've been working on for 1+ years Robots ground large language models in reality by acting as their eyes and hands while LLMs help robots execute long, abstract language instructions
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