- MolmoAct: Action Reasoning Models that can Reason in Space
- GraspMolmo: Generalizable Task-Oriented Grasping via Large-Scale Synthetic Data Generation
- PoliFormer: On-Policy RL with Transformers Results in Masterful Navigators
- Harmonic Mobile Manipulation: An End-to-End Learning Approach Combining Navigation and Manipulation
- Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Multimodal Models
- FLaRe: Achieving Masterful and Adaptive Robot Policies with Large-Scale Reinforcement Learning Fine-Tuning
- Open X-Embodiment: Robotic Learning Datasets and RT-X Models
- SPOC: Imitating Shortest Paths in Simulation Enables Effective Navigation and Manipulation in the Real World
Research Experience
Currently a research scientist at the Allen Institute for Artificial Intelligence (AI2) on the Perceptual Reasoning and Interaction (PRIOR) team, working on embodied AI in unstructured human environments.
Education
PhD in Mechanical Engineering, University of Washington, advised by Santosh Devasia and Joseph Garbini.
Background
Research interests: Bringing robotics into unstructured human environments, particularly through the use of simulation and synthetic data. Works at the Allen Institute for Artificial Intelligence (AI2) on the Perceptual Reasoning and Interaction (PRIOR) team, focusing on embodied AI in unstructured human environments.