Andrea Bajcsy
abajcsy [at] berkeley [dot] edu

I am starting as an Assistant Professor at Carnegie Mellon University in Fall 2023.

I am recruiting PhD students for Fall 2023! Check out more information on joining the lab here.

I am a postdoctoral scholar at the University of California, Berkeley working with Jitendra Malik.

I completed my PhD in Electrical Engineering & Computer Science at UC Berkeley. I was advised by Anca Dragan and Claire Tomlin. I spent Spring 2021 at NVIDIA in the Autonomous Vehicle Research Group, working with Marco Pavone.

I study safe human-robot interaction, particularly when robots learn from and learn about people. My research unites traditionally disparate methods from control theory and machine learning to develop theoretical frameworks and practical algorithms for human-robot interaction. I ground my work through a variety of applications, from assistive robotic arms, to quadrotors, to autonomous cars, and in experiments with real human participants.

email   |   cv   |   google scholar   |   github  |   bio

Unsure how to pronounce my last name? Bajcsy sounds like BYE-chee.

• [Jan 2023]

  Our paper on long-term robot influence on humans got accepted to ICRA 2023!

• [Jan 2023]

  Our paper on modeling & influencing the dynamics of human learning was accepted to HRI 2023!

• [Dec 2022]

  I am on the organizing committee of RSS 2023.

• [Dec 2022]

  I gave a guest lecture at Princeton's Safety-Critical Robotic Systems class.

• [Nov 2022]

  I am serving as an Area Chair for L4DC.

• [Oct 2022]

  I gave an invited talk at Nuro on practical safety for human-robot interaction.

• [Sept 2022]

  Paper submitted to ICRA on robots that influence humans over long-term interaction.

• [Sept 2022]

  I'm serving as Associate Editor for ICRA 2023.

• [Aug 2022]

  I completed my PhD and started my postdoc! My thesis entitled "Bridging Safety and Learning in Human-Robot Interaction" can be found here.

• [July 2022]

  I gave an invited talk at the ICML Workshop on Safe Learning for Autonomous Driving (SL4AD).

• [June 2022]

  I gave an invited talk at the RSS Workshop on Close Proximity Human-Robot Collaboration.

• [May 2022]

  I accepted an offer to join the Robotics Institute in the School of Computer Science at Carnegie Mellon University (CMU) starting Fall 2023!

[Older News]

[Mar 2022]

Our paper Safety Assurances for Human-Robot Interaction via Confidence-aware Game-theoretic Human Models was accepted to ICRA 2022!

[Nov 2021]

I gave a talk at the University of Washington's Robotics Colloqium.

[Oct 2021]

Paper published in IJRR! In this work we formalize how physical human interaction with a robot can be treated as a form of communication, enabling robots to learn objectives from human corrections.

[Oct 2021]

New paper on arXiv! In this work we propose that robots use confidence-aware game-theoretic models of human behavior when assessing the safety of a human-robot interaction.

[Aug 2021]

I am grateful to have been selected for the Rising Stars Academic Career Workshop in EECS.

[Aug 2021]

New paper on arXiv! In this work we advocate for the use of Hamilton Jacobi (HJ) reachability as a unifying mathematical framework for comparing existing safety concepts used throughout industry and academia, and propose ways to expand its modeling premises in a data-driven fashion.

[April 2021]

I'm excited to join NVIDIA for an internship in the Autonomous Driving Research group led by Marco Pavone.

[Mar 2021]

Our paper on analyzing human models that adapt online was accepted to ICRA 2021!

[Feb 2021]

I gave a talk at University of Pennsylvania on analyzing human models that adapt online.

[Dec 2020]

I gave a talk on safe robots which learn from (and about) humans at the University of Chicago Laboratory School for the middle and high-school robotics club.

[Dec 2020]

I gave a talk at ETH Zurich on introspective human motion prediction for safe robot autonomy.

* indicates equal contribution and co-authorship.

(Honorable Mention for the 2020 IEEE T-RO Best Paper Award)

(invited to special issue)

(invited to special issue)

(oral, acceptance rate 10%)

website adapted from here