Diffusion Policy: Visuomotor Policy Learning via Action Diffusion — HN submission

Wed, 15 Jan 2025 18:04:35 GMT

Show HN: Federation of robots collaboratively train an object manipulation model

This is a quickstart example using LeRobot and Flower that demonstrates how to train a diffusion model collaboratively across 10 individual nodes (each with its own dataset). This example uses the push-t dataset, where the task is to move a letter T object on top of another that is to remain static. The example it's pretty easy to run, and can do so efficiently if you have access to a recent gaming GPU. Although the diffusion model only take 2GB of VRAM (of course you can decide to scale it up), the compute needed to train them isn't negligible. For context, running the example until…

7 points; 0 discussion replies on HN. Paper and source history. A submission is not an endorsement.

Diffusion Policy: Visuomotor Policy Learning via Action Diffusion — mentioned by momofuku

Thu, 21 Sep 2023 00:43:01 GMT

Having been a member of the robot learning community both in grad school and now in industry, I'd actually like to rightfully attribute something here since it seems that TRI is (deservedly so, I will agree wholeheartely) receiving most of the praise: The core of these advancements are powered by Diffusion Policy [1], which Prof. Shuran Song's lab at Columbia (before she moved recently to Stanford) developed and pioneered. I'd suggest everyone to view the original project website [2], it has a ton of amazing real world challenging experiments. It was a community favorite for the Best Paper…

Paper and discussion history. A mention is not an endorsement.

Diffusion Policy: Visuomotor Policy Learning via Action Diffusion · HackerPapers discussions

Diffusion Policy: Visuomotor Policy Learning via Action Diffusion — HN submission

Diffusion Policy: Visuomotor Policy Learning via Action Diffusion — mentioned by momofuku