Amber Xie

I am a PhD student at Stanford. My research is supported by the NDSEG Fellowship. I was also awarded the NSF Graduate Research Fellowship.

Previously, I graduated from UC Berkeley with an MS in Computer Science and BA in Computer Science and Applied Math. I was fortunate to be advised by Pieter Abbeel, Stephen James, and Youngwoon Lee at BAIR.

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I'm interested in robot learning. In particular, I would like to build intelligent, adaptable, multi-purpose robots that can learn quickly and accomplish complex tasks. I've worked on a variety of projects in deep reinforcement learning, robotics, and generative models to this aim.

LAPP Language-Conditioned Path Planning
Amber Xie, Youngwoon Lee, Pieter Abbeel, Stephen James
CoRL 2023 Conference on Robot Learning, 2023
paper / website / code / video

We propose the domain of Language-Conditioned Path Planning (LAPP), where contact-awareness is incorporated into the path planning problem. As a first step, we propose Language-Conditioned Collision Functions (LACO).

LAMP Language Reward Modulation for Pretraining Reinforcement Learning
Ademi Adeniji, Amber Xie, Carmelo Sferrazza, Younggyo Seo, Stephen James, Pieter Abbeel
arXiv 2023
paper / code

LAMP pretrains a language-conditioned agent without human supervision using noisy VLM rewards and unsupervised reinforcement learning.

vectorfusion VectorFusion: Text-to-SVG by Abstracting Pixel-Based Diffusion Models
Ajay Jain*, Amber Xie*, Pieter Abbeel
CVPR 2023 The IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023
paper / website / gallery

VectorFusion generates infinitely scalable vector graphics (SVGs), pixel art and sketches from text using the pretrained Stable Diffusion model.

IRM Skill-Based Reinforcement Learning with Intrinsic Reward Matching
Ademi Adeniji*, Amber Xie*, Pieter Abbeel
arXiv 2022
paper / code

IRM leverages the skill discriminator from unsupervised RL pretraining to perform environment-interaction-free skill sequencing for unseen downstream tasks.

sim2seg Sim2Seg: End-to-end Off-road Autonomous Driving without Real Data
John So*, Amber Xie*, Sunggoo Jung, Jeffrey Edlund, Rohan Thakker, Ali-akbar Agha-mohammad, Pieter Abbeel, Stephen James
CoRL 2022 Conference on Robot Learning, 2022
paper / website / code / method vid / real-world vid

Segmentations are a concise, compressed representation for images. We show sim-to-real transfer through training an RL agent on image segmentations for off-road autonomous driving.

maugs Math Augmentation: How Authors Enhance the Readability of Formulas using Novel Visual Design Practices
Andrew Head, Amber Xie, Marti Hearst
CHI 2022 ACM Conference on Human Factors in Computing Systems, 2022
paper / video

We describe how authors alter the presentation of math formulas to make them more approachable, from colorization to labels, layout, and beyond.

🏆 Best Paper Award

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