Siddhartha Srinivasa
Biographic Data
| ID | 5127624 |
|---|---|
| NAME | Siddhartha Srinivasa |
| GIVEN NAMES | Siddhartha |
| FAMILY NAME | Srinivasa |
| SIGNATURE | SRINIVASA S |
| AFFILIATIONS | University of Washington, Seattle, WA |
| VERIFIED | No |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 2 |
| AUTHOR COUNT | 3 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2016 |
| LATEST PUBLICATION YEAR | 2020 |
| H-INDEX | 1 |
Trust-Aware Decision Making for Human-Robot Collaboration
Trust in autonomy is essential for effective human-robot collaboration and user adoption of autonomous systems such as robot assistants. This article introduces a computational model that integrates trust into robot decision making. Specifically, we learn from data a partially observable Markov decision process (POMDP) with human trust as a latent variable. The trust-POMDP model provides a principled approach for the robot to (i) infer the trust …
A Decision-Theoretic Approach for the Collaborative Control of a Smart Wheelchair
Viewpoint-based legibility optimization
Much robotics research has focused on intent-expressive (legible) motion. However, algorithms that can autonomously generate legible motion have implicitly made the strong assumption of an omniscient observer, with access to the robot's configuration as it changes across time. In reality, human observers have a particular viewpoint, which biases the way they perceive the motion. In this work, we free robots from this assumption and introduce the …
Viewpoint-based legibility optimization
Much robotics research has focused on intent-expressive (legible) motion. However, algorithms that can autonomously generate legible motion have implicitly made the strong assumption of an omniscient observer, with access to the robot's configuration as it changes across time. In reality, human observers have a particular viewpoint, which biases the way they perceive the motion. In this work, we free robots from this assumption and introduce the …
Viewpoint-based legibility optimization
Much robotics research has focused on intent-expressive (legible) motion. However, algorithms that can autonomously generate legible motion have implicitly made the strong assumption of an omniscient observer, with access to the robot's configuration as it changes across time. In reality, human observers have a particular viewpoint, which biases the way they perceive the motion. In this work, we free robots from this assumption and introduce the …
A Decision-Theoretic Approach for the Collaborative Control of a Smart Wheelchair
Trust-Aware Decision Making for Human-Robot Collaboration
Trust in autonomy is essential for effective human-robot collaboration and user adoption of autonomous systems such as robot assistants. This article introduces a computational model that integrates trust into robot decision making. Specifically, we learn from data a partially observable Markov decision process (POMDP) with human trust as a latent variable. The trust-POMDP model provides a principled approach for the robot to (i) infer the trust …
Artificial Intelligence (3 works) · Computer Science (3 works) · Human–computer interaction (3 works) · Robot (3 works) · Human–robot interaction (2 works) · Machine learning (2 works) · Markov chain (2 works) · Markov decision process (2 works) · Markov model (2 works) · Markov process (2 works)