Adam Wei @CoRL ✈️

Adam Wei @CoRL ✈️ Verified account @adamwei_ · Jun 18

🤖 We introduce Ambient Diffusion Policy, a simple and principled method for training policies with suboptimal data in robotics.

Suboptimal data is everywhere in robotics…
❌ Data filtering is wasteful
❌ Co-training learns both good and bad features
✅ Ambient Diffusion Policy selectively learns useful features via noise-dependent data usage

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Michael Zeng Verified account @michzeng_ · Aug 20

Action chunking — especially executing long action sequences open-loop — is widely used in imitation learning for robotic manipulation. Why is it so effective and do we really need it? We find a key reason:

Long open-loop execution helps short-context policies imitate non-Markovian experts.

With this insight, we show how to move beyond open-loop execution: extending policy context restores reactivity while achieving even higher task performance.

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Adam Wei @CoRL ✈️ Verified account @adamwei_ · Jun 18

🤖 We introduce Ambient Diffusion Policy, a simple and principled method for training policies with suboptimal data in robotics.

Suboptimal data is everywhere in robotics…
❌ Data filtering is wasteful
❌ Co-training learns both good and bad features
✅ Ambient Diffusion Policy selectively learns useful features via noise-dependent data usage

👇🧵(1/5)

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Abhinav Agarwal @_abhinav_ag · Jul 22

Roboticists have been working hard to add memory to “goldfish” imitation learning policies. But do we understand how context length impacts state-of-the-art imitation learning methods?

We provide an in-depth analysis of long-context Diffusion Policies to study this

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