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Since the first year of my PhD, every talk I’ve given has opened with a slide about the distant north star: dropping a robot in a home it’s never been before and having it do useful things. I think it might be time for me to find a new opening slide 😀. Thrilled to share π-0.5!

My goal throughout my PhD has been to take robots out of the lab and into the real world. It was so special to be a part of this effort and see this dream become reality! Excited to keep pushing model capabilities—and, of course, keep playing with robots 🤖

I’m in Singapore for #ICLR2025! Excited to present Improving Test-Time Search for LLMs with Backtracking Against In-Context Value Verifiers (openreview.net/pdf?id=ZXRKOAf…). Workshops: - Reasoning and Planning for LLMs — Oral Session April 28 - SSI-FM — Poster Happy to chat/meet up!

Introducing ✨Latent Diffusion Planning✨ (LDP)! We explore how to use expert, suboptimal, & action-free data. To do so, we learn a diffusion-based *planner* that forecasts latent states, and an *inverse-dynamics model* that extracts actions. w/ Oleg Rybkin Dorsa Sadigh Chelsea Finn

I'm giving two talks today/Sunday at #ICLR2025 ! - Post-Training Robot Foundation Models (Robot Learning Workshop @ 12:50 pm) - Robot Foundation Models with Open-Ended Generalization (Foundation Models in the Wild @ 2:30 pm) Will cover π-0, Demo-SCORE, Hi Robot, & π-0.5.

Most robot policies don't have any memory! This is because: - policies often perform *worse* with past observations as input - GPU memory and compute constraints We address both to train long-context robot diffusion policies. 🤖 Paper & code: long-context-dp.github.io

How do we make a scalable RL recipe for robots? We study batch online RL w/ demos. Key findings: - iterative filtered imitation is insufficient - need diverse policy data, eg using diffusion policy - policy extraction can hinder data diversity Paper: pd-perry.github.io/batch-online-r…

How can robots problem solve in novel environments? We combine high-level reasoning with VLMs with low-level controllers to allow test-time problem solving. Paper & code: anniesch.github.io/vlm-pc/

We still lack a scalable recipe for RL post-training seeded with demonstration data. Many methods add an imitation loss, but this constrains learning too much. We propose to use the demos only to perturb exploration -- It works really well! Paper: arxiv.org/abs/2506.07505