I am interested in scalable robot learning. Below are my works.
Pixie: Physics from Pixels
Long Le,
Ryan Lucas,
Chen Wang,
Chuhao Chen,
Dinesh Jayaraman,
Eric Eaton,
Lingjie Liu
Preprint, 2025
(
website,
code (coming soon!)
)
We predict 3D physics from distilled CLIP features, enabling generalization and fast inference across scenes.
Pixie produces physics that are 2.2–4.6× more realistic while running 10³× faster.
Articulate Anything
Long Le, Jason Xie, William Liang, Hung-Ju Wang, Yue Yang, Jason Ma, Kyle Vedder, Arjun
Krishna, Dinesh
Jayaraman, Eric Eaton
International Conference on Learning Representations (ICLR), 2025
(
website,
twitter,
paper,
code
)
A major bottleneck in scaling robot learning in simulation is the lack of interactable
3D environments. We present a SOTA method, leveraging VLMs to automatically generate articulated 3D models
from
any input modality including text, real-world images, or videos.
Distributed Continual Learning
Long Le, Marcel Hussing, and Eric Eaton
Preprint, 2024
(paper)
We study the intersection of continual and federated learning, in which independent agents face unique tasks.
We develop the mathematical formulation for the setting, develop algorithms for sharing data, model weights,
and modules, and provide extensive empirical results across network bandwidths, and topology.
A collective AI via lifelong learning and sharing at the edge
A Soltoggio et al
Nature Machine Intelligence, 2024
(paper)
We survey approaches and challenges in developing a collaborative life-long AI system on edge devices.
