Rose Hendrix

c@rihdrmnasmg.imxeo.ole.

I am a Senior Research Engineer at the Allen Institute for Artificial Intelligence (AI2) on the Perceptual Reasoning and Interaction (PRIOR) team, where I work on embodied AI in unstructured human environments. Prior to that, I earned my PhD in Mechanical Engineering at the University of Washington, where I was advised by Santosh Devasia and Joseph Garbini.

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Research

I'm interested in how to bring robotics into unstructured human environments. Here is my relevant recent work - please see my Google Scholar for a complete history. * denotes equal contribution.

PoliFormer: On-Policy RL with Transformers Results in Masterful Navigators

Policy TransFormer (PoliFormer) is a transformer-based policy trained using RL at scale in simulation. PoliFormer achieves SoTA results across LoCoBot and Stretch RE-1, in both simulation and real-world.

Harmonic Mobile Manipulation

HarmonicMM is an end-to-end learning approach that combines navigation and manipulation, significantly improving success rates in complex tasks like door opening and table cleaning, with successful real-world transfer of agents trained in simulation.

Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Multimodal Models

Molmo and PixMo present open weights and open data to advance multimodal AI models, pushing the boundaries of state-of-the-art performance across various tasks.

FLaRe: Achieving Masterful and Adaptive Robot Policies with Large-Scale Reinforcement Learning Fine-Tuning

FLaRe utilizes large-scale reinforcement learning fine-tuning to create adaptive and highly capable robot policies, achieving state-of-the-art results in both simulated and real-world environments.

Open X-Embodiment: Robotic Learning Datasets and RT-X Models

We assemble a dataset from 22 different robots collected through a collaboration between 21 institutions, demonstrating 527 skills (160266 tasks).

SPOC: Imitating Shortest Paths in Simulation Enables Effective Navigation and Manipulation in the Real World

We train a supervised model to imitate shortest path trajectories collected from simulation and show that it generalizes to perform effective navigation and manipulation when deployed on real world agents.

Phone2Proc: Bringing Robust Robots Into Our Chaotic World

From a 10-minute iPhone scan of any environment, we condition procedural scene generation on that scan to generate training environments. Training a robot to perform ObjectNav in these scenes dramatically improves sim-to-real performance from 35% to 71% and results in an agent that is remarkably robust to human movement, lighting variations, added clutter, and rearranged objects.

Original design and source code from Jon Barron, modified by Leonid Keselman, and email scrambler by Jeff Donohue.