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

science

hehe

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.

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.

😮🫤🤪🫥🤖

hehehe

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.

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)

our dextrah-rgb code is out! that includes our vectorized geometric fabrics library we've been using for safe control of the robot.

Our latest work performs sim2real dexterous grasping using end-to-end depth RL.

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.

super excited that we won the best student paper for videomimic! unfortunately i was packing in the hotel during the awards ceremony 😅