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Jianlan Luo

@jianlanluo • 1,904 subscribers

@berkeley_ai @Theteamatx, PhD from @UCBerkeley

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Long-horizon visual goals remain surprisingly hard for robot manipulation. We introduce Act2Goal, a goal-conditioned policy that uses a visual world model to reason about progress toward a goal, and practice it autonomously in the real world.

Long-horizon visual goals remain surprisingly hard for robot manipulation. We introduce Act2Goal, a goal-conditioned policy that uses a visual world model to reason about progress toward a goal, and practice it autonomously in the real world.

95,100 görüntüleme

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Excited to share LWD: Learning While Deploying. Our robots learn while doing real tasks—restocking groceries, brewing Gongfu tea, making cocktails, making juice, and packing shoes. Deployment is no longer just evaluation; it becomes the training loop. 🧵

Excited to share LWD: Learning While Deploying. Our robots learn while doing real tasks—restocking groceries, brewing Gongfu tea, making cocktails, making juice, and packing shoes. Deployment is no longer just evaluation; it becomes the training loop. 🧵

621,657 görüntüleme • 1 ay önce

Excited to release τ0-WM: an open-source unified video-action world model for robotic manipulation. It's a 5B-parameter robotic foundation model trained on 27.3K hours of real-robot teleoperation, UMI-style demonstrations, and egocentric interaction videos.

Excited to release τ0-WM: an open-source unified video-action world model for robotic manipulation. It's a 5B-parameter robotic foundation model trained on 27.3K hours of real-robot teleoperation, UMI-style demonstrations, and egocentric interaction videos.

52,755 görüntüleme • 4 gün önce

Generalist robots don’t fail due to a lack of generality. They fail due to a lack of proficiency where it matters. We introduce SOP, enabling generalist policies to improve from real-world experience across distributed robot fleets, without sacrificing generality. 🧵

Generalist robots don’t fail due to a lack of generality. They fail due to a lack of proficiency where it matters. We introduce SOP, enabling generalist policies to improve from real-world experience across distributed robot fleets, without sacrificing generality. 🧵

1,724,104 görüntüleme • 4 ay önce

Long-horizon visual goals remain surprisingly hard for robot manipulation. We introduce Act2Goal, a goal-conditioned policy that uses a visual world model to reason about progress toward a goal, and practice it autonomously in the real world.

Long-horizon visual goals remain surprisingly hard for robot manipulation. We introduce Act2Goal, a goal-conditioned policy that uses a visual world model to reason about progress toward a goal, and practice it autonomously in the real world.

95,100 görüntüleme • 5 ay önce

One core bottleneck in VLA models is action representation. Discrete tokens scale beautifully with VLM pretraining—but lose precision. Continuous actions are precise—but often break VLM reasoning. In our new work, we resolve this tension at the representation level. 🧵

One core bottleneck in VLA models is action representation. Discrete tokens scale beautifully with VLM pretraining—but lose precision. Continuous actions are precise—but often break VLM reasoning. In our new work, we resolve this tension at the representation level. 🧵

47,854 görüntüleme • 5 ay önce

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