Russ Tedrake
@RussTedrake
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Professor at MIT, studying robotics. Vice President of Robotics Research, Toyota Research Institute.
Joined July 2022
Very proud of Nicholas, who recently shared (for physics-quality assets from a small amount of interaction with a robot) and is now following up with his work on scene-level generation.
Want to scale robot data with simulation, but don’t know how to get large numbers of realistic, diverse, and task-relevant scenes?. Our solution:.➊ Pretrain on broad procedural scene data.➋ Steer generation toward downstream objectives. 🌐 🧵1/8
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In my mind, it's a bit like a biology paper that is focused on a particular animal model. I hope we'll learn more quickly from each other if we can make precise, substantiated claims about particular setups, so that as a field we can assemble those claims into a coherent picture.
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Side note: I'm proud of the title of this paper, which we intentionally made pretty narrow/specific. I think that some of the most important work that we have to do as a field right now is careful empirical work to interrogate the properties of these models that we're creating.
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One of the most interesting take-aways for me is that "high-performing policies need to know whether they are executing in sim or in real." A number of implications flow from that, including that sim+real cotraining can decrease performance if the visual gap is too small.
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This work really sharpened my thinking about sim+real cotraining.
Learning from both sim+real data could scale robot imitation learning. But what are the scaling laws & principles of sim+real cotraining?. We study this in the first focused analysis of sim+real cotraining spanning 250+ policies & 40k+ evals (1/6)
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RT @NicholasEPfaff: New Paper: "Scalable Real2Sim: Physics-Aware Asset Generation via Robotic Pick-and-Place Setups"! 🤖. We introduce a ful….
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RT @BoyuanChen0: Announcing Diffusion Forcing Transformer (DFoT), our new video diffusion algorithm that generates ultra-long videos of 800….
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I'm super excited to start a great new collaboration with the fantastic team at Boston Dynamics. Scott Kuindersma and I chatted with Evan Ackerman about it earlier today.
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RT @LerrelPinto: This #RSS2024 on July 19, we are organizing a tutorial on supervised policy learning for real world robots!. Talks by @not….
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RT @BoyuanChen0: Introducing Diffusion Forcing, which unifies next-token prediction (eg LLMs) and full-seq. diffusion (eg SORA)! It offers….
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RT @chris_j_paxton: And if you’re at all interested in humanoids you need to check out Punyo, from TRI, a soft-body humanoid capable of who….
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RT @KennethCassel: a bit surprised TRIs new robot has gotten little love on X . current humanoid approaches seem very hand centric but we u….
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RT @SongShuran: Check out @chichengcc's step-by-step tutorial on building the UMI gripper. We really hope to see more UMIs running in the w….
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RT @chichengcc: Can we collect robot data without any robots?. Introducing Universal Manipulation Interface (UMI). An open-source $400 syst….
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