01:02:56 · Lawrence Zhu, Pranav, Chris Paxton, Michael Cho
[01:02:56] S1: Yeah. It's actually a... We have two concurrent projects. And if you look at the author list, they're actually flipped. Because... Yeah. So, I'm gonna recommend this paper called, this paper called ego bridge domain adaptation for generalizable imitation from, egocentric human data. So same team, author list flipped, basically. And in that paper, we pretty much tried to, leverage, the... We tried to leverage dynamic time warping and optimal transport to solve direct transferring human knowledge into robots so that we achieve scene generalization zero shot and motion generalization zero shot. It's a really interesting paper. Yep. [01:03:42] S2: Yeah. So, like, if you think about it, like, inside of the latent space of, like, a model, you have like the human distribution and the robot distribution. It's like, how can we align those two distributions? How can we bring them together so that in the model you kind of see it very similarly, and then you can learn how to learn different behaviors like motions and tasks in a much better way. [01:04:02] S0: Got it. [01:04:03] S2: Yeah, I don't think we should spoil too much, Lawrence. So I think, I think, you can visit us in NeurIPS or, you can check out the paper. [01:04:10] S0: Cool. Cool. Cool. Yeah. Definitely this one. Alright. Cool. I guess, yeah, that... That's a wrap. Thank you so much for making the time, guys. I really appreciate it. Really fascinating one. You. [01:04:18] S1: Thanks so much for having us. [01:04:20] S2: Covering EMMA. [01:04:21] S3: Yeah. Thank you, guys. [01:04:22] S1: Cool. Alright.