Bernadette Bucher

I am a PhD Student in the GRASP lab at University of Pennsylvania advised by Dr. Kostas Daniilidis.

My research interests broadly lie in developing meaningful representations of sensory data in robotic systems for intelligent autonomous decision making. My current work focuses on neuromorphic approaches to perceptual decision making.

Prior to starting my PhD, I was a Senior Software Engineer at Lockheed Martin Corporation where I worked from 2014 to 2019. I received an M.A. in Mathematics, M.A. in Economics, and B.S. in Mathematics and Economics from The University of Alabama in 2014.

Email  /  CV  /  Biography  /  Google Scholar  /  LinkedIn

profile photo
fast-texture RoboNet: Large-Scale Multi-Robot Learning
Sudeep Dasari, Frederik Ebert, Stephen Tian, Suraj Nair, Bernadette Bucher, Karl Schmeckpeper, Siddharth Singh, Sergey Levine, Chelsea Finn
Conference on Robot Learning (CoRL), 2019
arXiv  /  project page  /  code  /  bibtex

We developed a dataset of over 15 million video frames of 7 different robots at 113 different camera viewpoints interacting with objects. We use our new dataset to test the generalization capability of state-of-the-art video prediction algorithms.

fast-texture Perception-Driven Curiosity with Bayesian Surprise
Bernadette Bucher, Anton Arapin, Ramanan Sekar, Feifei Duan, Marc Badger, Kostas Daniilidis, Oleh Rybkin
RSS Workshop on Combining Learning and Reasoning Towards Human-Level Robot Intelligence, 2019

We model scene dynamics with a conditional variational autoencoder from which we compute an intrinsic reward for curiosity for use in a reinforcement learning algorithm.

clean-usnob Unsupervised Monocular Depth And Latent Structure
Kenneth Chaney*, Bernadette Bucher*, Evangelos Chatzipantazis, Jianbo Shi, Kostas Daniilidis
CVPR Workshop on 3D Scene Understanding for Vision, Graphics, and Robotics, 2019

We demonstrate a novel method for learning distinct latent representations of structural and semantic information from single monocular images which we use for novel viewpoint synthesis.

An inspirational website.