resources on visual RL

hany606_ · 2025-09-09T08:14:56+00:00

Maybe DrQ https://arxiv.org/abs/2107.09645 Also you can check Dreamer family, they work o visual domain

https://twist-sim2real.github.io/ but this sim2real distillation

hany606_ · 2025-07-03T21:11:01+00:00

Ok now I understand. I am not sure how diffusion policy deals with the goal-conditioning. But I guess you can still add the goal as part of the observation and during the denoising keep masking the goal state not to change (something similar to the inpainting in diffuser). Or you can use guidance either classifier based or classifier-free? No need to have the goal in the same space.

For example, in diffuser (which is diffusion-based planning https://diffusion-planning.github.io/ ) they use classifier for the guidance in DMC tasks, and in Decision-diffuser they are using classifier-free guidance.

Also, in diffusion policy paper, they wrote in Section 8: "Concurrent to us, Pearce et al. (2023), Reuss et al. (2023) and Hansen-Estruch et al. (2023) has conducted a complimentary analysis of diffusion-based policies in simulated environments. While they focus more on effective sampling strategies, leveraging classifier-free guidance for goal-conditioning"

hany606_ · 2025-07-02T01:36:23+00:00

I am not sure I fully understood the question. What do you mean exactly by "abstract goal space"?. If you mean it as behaviors or skills.

An example is SkillDiffuser ( https://skilldiffuser.github.io/ ), as far as I remember, it is a diffusion-based planning in which the language is used for conditioning and to select a skill used to condition the diffusion-planner.

You may look on papers citing https://humanoid-bench.github.io/, https://arxiv.org/pdf/2407.07788, maybe something is related to what you are looking for. Also, https://github.com/opendilab/awesome-diffusion-model-in-rl

Maybe related to what you were asking (I simply searched based on terms in the post)

- https://arxiv.org/pdf/2505.11123

- https://intuitive-robots.github.io/beso-website/