Shreyas Bhat

Shreyas Bhat

Research Interests - Human-Robot Interaction, Trust, Adaptability

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Pages

Posts

Future Blog Post

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This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false. Read more

Blog Post number 4

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Blog Post number 3

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Blog Post number 2

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Blog Post number 1

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projects

Course Project - IOE 691 Bayesian Data Science - Weight Uncertainty in Neural Networks

In this project, we tried to replicate the results from the paper ‘Weight Uncertainty in Neural Networks’ that tries to add regularization to neural networks by treating the weights as probability distributions and learning them in a Bayesian way

We train and evaluate a Bayesian fully connected neural network on the MNIST dataset and implement weight pruning following the paper… Read more

Feedback Linearization Based Trajectory Planning for a Planar Two-Link Bipedal Walking Robot

The goal of this study is to perform a solution space analysis for the biped trajectory planning method using feedback linearization. As a first step, one such trajectory was generated during this phase of the study. The concept of feedback linearization was used to convert the highly complex and nonlinear problem into a relatively easy to solve linear differential equation.

At this stage, a solution to the problem that minimizes the energy input was found and in that process, all the background code that is required for the continuation of the study was set up… Read more

Trust Dynamics with Discounting of Performance History

In an earlier project, along with some collaborators, we found three distinct types of trust dynamics exhibited by people when interacting repeatedly with robots and automation. The figure below shows the three clusters using two distinguishing features: The root mean squared error \(E_{RMS}\) between the trust reports given by the participants and the values predicted by a performance-based trust model and the average logarithm of trust reports given by the participants.

In this project, we focus on the oscillators cluster… Read more

I-ORCA: Implicitly Nudging Human Trajectories

In this project, we look at the inverse of the social navigation problem. Social Navigation deals with generating trajectories for mobile robots that minimally invade human trajectories.

We try to see if we can leverage the obstacle avoidance nature of humans to generate trajectories for robots that minimally nudge the humans toward desired directions… Read more

Course Project - IOE 536 Cognitive Ergonomics - Redesigning Venmo

In this project, we analyzed the Venmo app from the perspective of cognitive ergonomics. We found a lot of inconsistent and irrational design choices leading to bad user experience. We proposed a few redesigns that will help make the user experience better.

. The main thing we tried to remove was the ‘social media’ aspect of Venmo… Read more

Course Project - EECS 545 Machine Learning - Solving Wordle using Reinforcement Learning

As a part of this course project, we used deep reinforcement learning (DeepRL) to try and solve the word game Wordle. In wordle, you have 6 chances to guess a 5-letter word. After each guess, you get feedback about the correct letters and positions until you guess the correct word.

. We implemented two versions of the model, one using a dictionary of words as an input and the other guessing one character at a time, without knowing a dictionary… Read more

Estimating Readiness of Drivers to Takeover Control of a Vehicle in Conditionally Automated Driving

I worked as a software developer for this project, helping the lead PhD student implement sensor fusion from a variety of sensors and computing some physiological metrics in real-time using the Robot operating system (ROS) in python. The iMotions software combines various sources of data (GSR sensor, eye-tracker, and heart-rate sensor) and communicates to a computer running the following ROS framework using UDP.

The computer can then compute the metrics and send it to another server running a machine learning model to predict the driver’s readiness in real-time… Read more

Trust-Driven Human-Robot Interaction

In this project, we study how trust evolves when humans repeatedly interact with a robot recommendation system. We try to leverage quantitative models of trust to predict human behavior and design interaction strategies for the robot to promote trust in the robot’s recommendations.

We model the interaction as a trust-aware Markov Decision Process (trust-aware MDP) that consists of States, Actions, Transition Function, Reward Function, and Human Behavior Model… Read more

Trustworthy Human-AGV Interaction

In this project, we try to generate trustworthy behaviors for Automated Guided Vehicles (AGVs) while interacting with human workers in shared workspaces in manufacturing plants.

As a first step, we modeled worker motion as a Finite Automaton Model (FAM) with 6 intuitive states and transition functions that could be set up easily once we get the plan of the manufacturing plant. As a second step, we have added predictive modeling to the basic FAM model… Read more

publications

Clustering Trust Dynamics in a Human-Robot Sequential Decision-Making Task

Published in IEEE International Conference on Robotics and Automation (ICRA), 2022

In this paper, we present a framework for trust-aware sequential decision-making in a human-robot team. We model the problem as a finite-horizon Markov Decision Process with a reward-based performance metric, allowing the robotic agent to make trust-aware recommendations. Results of a human-subject experiment show that the proposed trust update model is able to accurately capture the human agent’s moment-to-moment trust changes. Moreover… Read more

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Clustering Trust Dynamics in a Human-Robot Sequential Decision-Making Task

Published in IEEE Robotics and Automation Letters, 2022

In this paper, we present a framework for trust-aware sequential decision-making in a human-robot team wherein the human agent’s trust in the robotic agent is dependent on the reward obtained by the team. We model the problem as a finite-horizon Markov Decision Process with the trust of the human on the robot as a state variable. We develop a reward-based performance metric to drive the trust update model, allowing the robotic agent to make trust-aware recommendations… Read more

Recommended citation: S. Bhat, J. B. Lyons, C. Shi and X. J. Yang, "Clustering Trust Dynamics in a Human-Robot Sequential Decision-Making Task," in IEEE Robotics and Automation Letters, vol. 7, no. 4, pp. 8815-8822, Oct. 2022, doi: 10.1109/LRA.2022.3188902.
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Effect of Adapting to Human Preferences on Trust in Human-Robot Teaming

Published in AAAI Symposium Series, 2024

We present the effect of adapting to human preferences on trust in a human-robot teaming task. The team performs a task in which the robot acts as an action recommender to the human. It is assumed that the behavior of the human and the robot is based on some reward function they try to optimize. We use a new human trust-behavior model… Read more

Recommended citation: Bhat, S., Lyons, J. B., Shi, C., & Yang, X. J. (2024). Effect of Adapting to Human Preferences on Trust in Human-Robot Teaming. Proceedings of the AAAI Symposium Series, 2(1), 5-10. https://doi.org/10.1609/aaaiss.v2i1.27642
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Evaluating the impact of personalized value alignment in human-robot interaction Insights into trust and team performance outcomes

Published in ACM/IEEE International Conference on Human-Robot Interaction, 2024

This paper examines the effect of real-time, personalized alignment of a robot’s reward function to the human’s values on trust and team performance. We present and compare three distinct robot interaction strategies: a non-learner strategy where the robot presumes the human’s reward function mirrors its own; a non-adaptive-learner strategy in which the robot learns the human’s reward function for trust estimation and human behavior modeling, but still optimizes… Read more

Recommended citation: Shreyas Bhat, Joseph B. Lyons, Cong Shi, and X. Jessie Yang. 2024. Evaluating the Impact of Personalized Value Alignment in Human-Robot Interaction: Insights into Trust and Team Performance Outcomes. In Proceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction (HRI '24). Association for Computing Machinery, New York, NY, USA, 32–41. https://doi.org/10.1145/3610977.3634921
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Value alignment and trust in human-robot interaction: Insights from simulation and user study

Published in Discovering the Frontiers of Human-Robot Interaction. Springer, Cham., 2024

With the advent of AI technologies, humans and robots are increasingly teaming up to perform collaborative tasks. To enable smooth and effective collaboration, the topic of value alignment (operationalized herein as the degree of dynamic goal alignment within a task) between the robot and the human is gaining increasing research attention. Prior literature on value alignment makes an inherent assumption that aligning the values of the robot with that of the human benefits the team. This assumption, however… Read more

Recommended citation: Bhat, S., Lyons, J.B., Shi, C., Yang, X.J. (2024). Value Alignment and Trust in Human-Robot Interaction: Insights from Simulation and User Study. In: Vinjamuri, R. (eds) Discovering the Frontiers of Human-Robot Interaction. Springer, Cham. https://doi.org/10.1007/978-3-031-66656-8_3
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Identifying Worker Motion Through a Manufacturing Plant: A Finite Automaton Model

Published in 2024 33rd IEEE International Conference on Robot and Human Interactive Communication (ROMAN), 2024

Autonomous Guided Vehicles (AGVs) are becoming increasingly common in industrial environments to transport heavy equipment around warehouses. Within the idea of Industry 5.0, these AGVs are expected to work alongside humans in the same shared workspace. To enable smooth and trustworthy interaction between workers and AGVs, the AGVs must be able to model the workers’ behavior and plan their trajectories… Read more

Recommended citation: S. Yang et al., "Identifying Worker Motion Through a Manufacturing Plant: A Finite Automaton Model," 2024 33rd IEEE International Conference on Robot and Human Interactive Communication (ROMAN), Pasadena, CA, USA, 2024, pp. 1970-1977, doi: 10.1109/RO-MAN60168.2024.10731360.
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talks

I-ORCA – Implicitly Nudging Human Trajectories

Published:

Presented work done during the summer internship at Honda Research Institute in San Jose, CA. The work tackled a novel problem: How should we design robot trajectories that implicitly nudge human trajectories in desired directions using their collision avoidance behavior. Toward this, we inverted the well-known Optimal Reciprocal Collision Avoidance (ORCA) algorithm for modeling collision-avoiding agents. Read more

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Trustworthy Human-AGV Interaction

Published:

Presented the work done in the previous work for the project sponsored by Boeing on “Trustworthy Human-AGV Interaction” along with Doo Won Han. We presented two aspects: modeling and predction of worker motion inside manufacturing plants (Shreyas Bhat and Shaoze Yang, presented by Shreyas Bhat) and design and evaluation of external Human-Machine Interface (eHMI) (presented by Doo Won Han) for Automated Guided Vehicles (AGVs). Read more

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teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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