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There’s a lot of awesome research about LLM reasoning right now. But how is  learning in the physical world 🤖different than in language 📚? In a new paper, show that imitation learning in continuous spaces can be exponentially harder than for discrete state spaces, even when

Attending #ICLR2025 next week! I will be presenting Diffusion Policy Policy Optimization (DPPO) at the Friday morning poster session with Lars Ankile diffusion-ppo.github.io I also joined Physical Intelligence lately. Love to chat about what we've been up to at Pi!

Check out our newest work in bringing robots closer to open-world generalization! It was truly amazing to see (1) data scaling and (2) iterating over the cross-embodiment co-training recipe solved the tasks that the robot struggled with when I first joined Pi.

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

Guided Data Collection via Factored Scaling Curves Provides a principled method for deciding what data to collect and how much to collect for each factor by constructing factored scaling curves

Data is the fuel that drives robot learning, but we don't have great strategies for figuring out what data to collect to enable strong generalization. Check out Lihan Zha's first paper as a PhD student at Princeton University! 𝐆𝐮𝐢𝐝𝐢𝐧𝐠 𝐃𝐚𝐭𝐚 𝐂𝐨𝐥𝐥𝐞𝐜𝐭𝐢𝐨𝐧 𝐯𝐢𝐚

In the era of generalist robot foundation models, how do you get their pre-trained model to work well on your robot and task? 🌐factored-data-scaling.github.io 📈 We introduce Factored Scaling Curves (FSC): a principled approach for modeling how policy performance scales with data for

📢 Excited for the second workshop on Out-of-Distribution Generalization in Robotics: Towards Reliable Learning-based Autonomy at RSS! #RSS2025 🎯 How can we build reliable robotic autonomy for the real world? 📅 Short papers due 05/25/25 🌐 tinyurl.com/rss2025ood 🧵(1/4)

Nice to see the use of ManiSkill3 in this work! Simulation is not just useful for RL training. It provides some good cheap deterministic test beds, perfect for testing imitation learning scaling laws at scale. Years of data in hours

How to build vision-language-action models that train fast, run fast & generalize? In our new paper, we formalize & analyze the approach of our π-0.5 model & further improve it with a single stage recipe. Blog: pi.website/research/knowl… Paper: pi.website/download/pi05_…

Our newest VLA training recipe achieves fast training, fast inference, and great performance, by carefully designing the interface between model backbone and continuous actions. Many lessons learned along the way👇

Always fun to chat about generalization with Ani :)

Our models need to run in real time on real robots, but inference with big VLAs takes a long time. We developed Real-Time Action Chunking (RTC) to enable real-time inference with flow matching for the π0 and π0.5 VLAs! More in the thread👇

In LLM land, a slow model is annoying. In robotics, a slow model can be disastrous! Visible pauses at best, dangerously jerky motions at worst. But large VLAs are slow by nature. What can we do about this? An in-depth 🧵:

Join us at two workshops #RSS2025 on 6/21! 📍 Resource Constrained Robotics (RTH109) 🗣️ Oral talk: 11:00–11:15 📍 Continual Robot Learning from Humans (OHE132) 🖼️ Spotlight poster: 10:30–11:00 Come by and chat—we’re excited to share our work!

We’re releasing the RoboArena today!🤖🦾 Fair & scalable evaluation is a major bottleneck for research on generalist policies. We’re hoping that RoboArena can help! We provide data, model code & sim evals for debugging! Submit your policies today and join the leaderboard! :) 🧵