Nathan Ratliff @robot_trainer
Director of Robotic Systems @NVIDIA. Isaac Cortex, cobots, geometric methods; PhD CMU, research Max Planck, TTI-C, co-founder Lula Robotics, eng Google, Amazon linkedin.com/in/nathan-ratl… Seattle, WA Joined December 2010-
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isaac lab is an enabler! without it's tiled rendering dextrah-rgb wouldn't be possible. sim2real rl is the holy grail of robotics, and isaac lab brings together the perfect combination of technologies to legitimately start breaking that into a reality.
isaac lab is an enabler! without it's tiled rendering dextrah-rgb wouldn't be possible. sim2real rl is the holy grail of robotics, and isaac lab brings together the perfect combination of technologies to legitimately start breaking that into a reality.
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Nathan Ratliff retweeted
Our latest work performs sim2real dexterous grasping using end-to-end depth RL.
our dextrah-rgb code is out! that includes our vectorized geometric fabrics library we've been using for safe control of the robot.
our dextrah-rgb code is out! that includes our vectorized geometric fabrics library we've been using for safe control of the robot.
this is really cool. i've always thought learning-based methods were the right approach to global motion generation. nice work! (and all the demos! super robust and general system)
this is really cool. i've always thought learning-based methods were the right approach to global motion generation. nice work! (and all the demos! super robust and general system)
Nathan Ratliff retweeted
Regardless of whether you plan to use them in applications, everyone should learn about Gaussian processes, and Bayesian methods. They provide a foundation for reasoning about model construction and all sorts of deep learning behaviour that would otherwise appear mysterious.
hehehe
😮🫤🤪🫥🤖
make sure you expert makes mistakes and has to explore. there's been a lot of work around ensuring demonstrators have the same information as the robot, but this works shows it's super useful for the demonstrator to have less! super interesting.
make sure you expert makes mistakes and has to explore. there's been a lot of work around ensuring demonstrators have the same information as the robot, but this works shows it's super useful for the demonstrator to have less! super interesting.
andrew's explanations are always lucid and insightful. recommend taking a look. deep nets have soft (but flexible) inductive biases preferring simple explanations, and they're able to characterize that rigorously pulling out some decades old theory. super cool.
andrew's explanations are always lucid and insightful. recommend taking a look. deep nets have soft (but flexible) inductive biases preferring simple explanations, and they're able to characterize that rigorously pulling out some decades old theory. super cool.
hehe
science
good points. openai created llms at scale well before chatgpt, but chatgpt made them accessible and that was arguably more impactful. all the recent models have been technically spectacular, but making them universally accessible may again be felt (significantly) more strongly by…
good points. openai created llms at scale well before chatgpt, but chatgpt made them accessible and that was arguably more impactful. all the recent models have been technically spectacular, but making them universally accessible may again be felt (significantly) more strongly by…
Nathan Ratliff retweeted
It is remarkable that a committee of deliberating AI models can diagnose patients, propose the next tests, use the results of the tests to improve the diagnosis, order more tests, and eventually arrive at diagnoses, outperforming doctors. It is a specific setting, but nonetheless…
It is remarkable that a committee of deliberating AI models can diagnose patients, propose the next tests, use the results of the tests to improve the diagnosis, order more tests, and eventually arrive at diagnoses, outperforming doctors. It is a specific setting, but nonetheless…
Chris Paxton @chris_j_paxton
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