๐ฅ๐ผ๐ฏ๐ผ๐๐ ๐ฎ๐ฟ๐ฒ ๐ป๐ผ ๐น๐ผ๐ป๐ด๐ฒ๐ฟ ๐ฝ๐ฟ๐ผ๐ด๐ฟ๐ฎ๐บ๐บ๐ฒ๐ฑ.
— Pascal Bornet (@pascal_bornet) 30 mars 2026
๐ง๐ต๐ฒ๐ ๐ฎ๐ฟ๐ฒ ๐๐ฟ๐ฎ๐ถ๐ป๐ฒ๐ฑ.
Thatโs the real shift Iโm seeing from NVIDIAโs GTC.
Using Isaac Lab, robots are learning through reinforcement learning in simulation:
โช๏ธ Millions of trials
โช๏ธ No step-by-stepโฆ pic.twitter.com/JcrIMGW0Lw
๐ฅ๐ผ๐ฏ๐ผ๐๐ ๐ฎ๐ฟ๐ฒ ๐ป๐ผ ๐น๐ผ๐ป๐ด๐ฒ๐ฟ ๐ฝ๐ฟ๐ผ๐ด๐ฟ๐ฎ๐บ๐บ๐ฒ๐ฑ. ๐ง๐ต๐ฒ๐ ๐ฎ๐ฟ๐ฒ ๐๐ฟ๐ฎ๐ถ๐ป๐ฒ๐ฑ. Thatโs the real shift Iโm seeing from NVIDIAโs GTC. Using Isaac Lab, robots are learning through reinforcement learning in simulation: โช๏ธ Millions of trials โช๏ธ No step-by-step instructions โช๏ธ Learning by reward and feedback Thatโs how a machine learns to drive, jump, flipโฆ and recover. What stands out to me is this: ๐ช๐ฒโ๐ฟ๐ฒ ๐บ๐ผ๐๐ถ๐ป๐ด ๐ณ๐ฟ๐ผ๐บ ๐ฐ๐ผ๐ฑ๐ฒ โ ๐น๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด. And once systems can learn, they donโt just execute. They adapt. Same shift we saw with LLMs. Now itโs happening in the physical world. ๐ฆ๐ผ ๐ต๐ฒ๐ฟ๐ฒโ๐ ๐บ๐ ๐พ๐๐ฒ๐๐๐ถ๐ผ๐ป: Are we ready for machines that improve faster than we can program them? #ai #robotics #reinforcementlearning #nvidia #gtc #futureofwork
โ View original post on X โ @pascal_bornet, 2026-03-30 05:01 UTC
