Ravi Pandya

Robotics PhD Student

Carnegie Mellon University

As of fall 2020, I am a PhD student in the Robotics Institute at Carnegie Mellon University advised by Prof. Changliu Liu and Prof. Andrea Bajcsy. I am grateful to be funded by the NSF Graduate Research Fellowship.

Previously, I was a data scientist at the Global AI Accelerator (GAIA) within Ericsson.

As an undergrad at Berkeley, I primarily worked with Prof. Anca Dragan, but I also had the privilege of working in Prof. Ruzena Bajcsy's and Prof. Ron Fearing's labs.

Please see my Google Scholar for an up-to-date list of publications.


  • Safe Control
  • Human-Robot Interaction


  • (current) PhD in Robotics, 2020-present

    Carnegie Mellon University

  • BS in Electrical Engineering and Computer Science, 2015-2019

    UC Berkeley



PhD Student

The Robotics Institute at Carnegie Mellon University

Aug 2020 – Present Pittsburgh, PA
Working towards a PhD in robotics under Prof. Changliu Liu and Prof. Andrea Bajcsy.

Data Scientist

Ericsson Global AI Accelerator

Sep 2019 – Sep 2020 Santa Clara, California
Used multi-agent deep reinforcement learning to optimize radio network performance. Outlined and implemented production ready Python code for life cycle management of machine learning models.

Undergraduate Researcher

University of California, Berkeley

Dec 2016 – May 2019 Berkeley, California
Worked in multiple robotics labs on human-robot interaction and related problems.


Robust Safe Control with Multi-Modal Uncertainty

We introduce a least-conservative robust safe controller for dynamical systems with additive and multiplicative multimodal uncertainty for CBF-like safe control methods. We test our method on a simulated segway robot and find it is less conservative than existing unimodal robust control methods.

Multi-Agent Strategy Explanations for Human-Robot Collaboration

We introduce a novel method for generating explanations of collaborative strategies for humans and robots in tasks with multiple Nash equilibria. We generate a visual state-based explanation of what each agent should do in an upcoming collaboration. Ultimately, we find that our explanations help real participants better explore the full space of strategies and collaborate with autonomous partners more quickly.

Towards Proactive Safe Human-Robot Collaborations via Data-Efficient Conditional Behavior Prediction

We formulate a novel modification to typical human intention prediction via Bayesian inference that accounts for the influence that the robot will have on the person. Using this conditional behavior prediction model, the robot can proactively influence a human collaborator to choose efficient actions for the task. We find in a user study that participants tend to enjoy collaborating with this algorithm over baselines.

Multimodal Safe Control for Human-Robot Interaction

We derive a least-conservative robust safe controller for dynamical systems with additive multimodal uncertainty (where additive refers to how the uncertainty enters into the dynamics with respect to the control input). We test our controller on a simulated human-robot system where the robot is uncertain of the human’s goal and find this approach to be safer than existing maximum-likelihood-based unimodal robust controllers.

Safe and Efficient Exploration of Human Models During Human-Robot Interaction

We study the problem of adapting a robot’s dynamics model of a human collaborator online while staying safe; we test out controllers with different risk preferences and measure how they are affected by the presence of safe control. Ultimately, we find that a risk-seeking control can learn a good model, but necessitates activating the safety controller more than other methods.

Nonverbal Robot Feedback for Human Teachers

We study the problem of enabling a robot learner to give nonverbal feedback to a human teacher. We focus on using gaze as a predictor of the human teacher’s next action and find in simulation that this approach leads to faster and more accurate task learning. In both online and in-person user studies, we find that this nonverbal feedback also helps real human teachers get a better mental model of the robot learner and helps improve the robot’s learning performance.

Human-AI Learning Performance in Multi-Armed Bandits

We study how an AI agent can assist a human by suggesting options in a multi-armed bandit problem when both agents are learning the reward from arms from scratch. We find in a user study that people have two main modes of selecting arms that can be distibguished by the entropy of the arm frequencies over time, and that participants matched with an assistant with similar entropy profiles will be most helpful to them.

Learning Image-Conditioned Dynamics Models for Under-actuated Legged Millirobots

We enable a small underactuated robot to learn how to walk on different terrains with a small amount of data collected in the real world by training a neural network dynamics model and running MPC over it to track trajectories. Importantly, the dynamics model takes in images of the environment to condition on, allowing the robot to learn different gaits for different terrains with just a single model.

Learning Human Ergonomic Preferences for Handovers

We focus on understanding how to best learn ergonomic preferences from a human in object handovers, since each person will have individual comfort preferences or constraints. We study an active learning approach to learning a human ergonomic cost function compared to passive and random baselines, and find that while active learning estimates the human’s cost function quickly, it incurs a higher ergonomic cost during learning.

Recent Talks

Nonverbal Robot Feedback for Human Teachers

CoRL 2019 Oral, Acceptance: 5.3%