second year PhD student at Georgia Tech @ICatGT working on 3D scene representations and foundation models | MS from @berkeley_aialbertwilcox.github.io Atlanta, GAJoined April 2016
Heading to Seoul to present this work at CoRL 2025! Looking forward to lots of fun discussions this weekend. If you want to chat sometime please send a DM!
Heading to Seoul to present this work at CoRL 2025! Looking forward to lots of fun discussions this weekend. If you want to chat sometime please send a DM!
Robots struggle to learn new skills from human videos.
Why? We found that naive co-training produces disjoint distributions.
Our EgoBridge (NeurIPS’25) extends Optimal Transport to align human-robot latents, improving success by 44% and generalization to human-only tasks!🧵
Can we scale up mobile manipulation with egocentric human data?
Meet EMMA: Egocentric Mobile MAnipulation
EMMA learns from human mobile manipulation + static robot data — no mobile teleop needed!
EMMA generalizes to new scenes and scales strongly with added human data.
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Introducing Importance Weighted Retrieval, a simple modification to existing retrieval methods! Our importance sampling inspired approach helps us more effectively retrieve from prior datasets for few shot imitation learning!
#CoRL2025 Oral w/ Rahul Chand @DorsaSadigh@JoeyHejna
Most imitation learning policies break when the camera moves or the robot changes.
NOT THIS ONE 👇
[📍 Bookmark for later ]
A new 3D scene representation encoder, tackles this by enabling zero-shot generalization to unseen embodiments and viewpoints…
And it works with any IL…
🤖✨ What if models that take action in the physical world could think through your instructions? Meet MolmoAct, our new fully open Action Reasoning Model (ARM) that does just that. 🧵
Imitation learning frameworks are often with 2D inputs.
but 2D limits generalization even to camera poses.
This has been an ongoing challenge, especially for humanoids since the camera pose is not steady and need not match the training data.
@albertwilcoxiii has been working on…
Tired of slow-moving robots? Want to know how learning-driven robots can move closer to industrial speeds in the real world?
Introducing SAIL - a system for speeding up the execution of imitation learning policies up to 3.2x on real robots. A short thread:
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