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6 results on '"Agrawal, Shivendra"'

1. ShelfHelp: Empowering Humans to Perform Vision-Independent Manipulation Tasks with a Socially Assistive Robotic Cane

Author

Agrawal, Shivendra, Nayak, Suresh, Naik, Ashutosh, and Hayes, Bradley

Subjects
Computer Science - Robotics, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Human-Computer Interaction, Computer Science - Machine Learning
Abstract

The ability to shop independently, especially in grocery stores, is important for maintaining a high quality of life. This can be particularly challenging for people with visual impairments (PVI). Stores carry thousands of products, with approximately 30,000 new products introduced each year in the US market alone, presenting a challenge even for modern computer vision solutions. Through this work, we present a proof-of-concept socially assistive robotic system we call ShelfHelp, and propose novel technical solutions for enhancing instrumented canes traditionally meant for navigation tasks with additional capability within the domain of shopping. ShelfHelp includes a novel visual product locator algorithm designed for use in grocery stores and a novel planner that autonomously issues verbal manipulation guidance commands to guide the user during product retrieval. Through a human subjects study, we show the system's success in locating and providing effective manipulation guidance to retrieve desired products with novice users. We compare two autonomous verbal guidance modes achieving comparable performance to a human assistance baseline and present encouraging findings that validate our system's efficiency and effectiveness and through positive subjective metrics including competence, intelligence, and ease of use., Comment: 8 pages, 14 figures and charts

Published
2024
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4. Explanation-based Reward Coaching to Improve Human Performance via Reinforcement Learning.

Author

Tabrez, Aaquib, Agrawal, Shivendra, and Hayes, Bradley

Subjects
REINFORCEMENT learning, HUMAN beings, ARTIFICIAL intelligence, HUMAN experimentation
Abstract

For robots to effectively collaborate with humans, it is critical to establish a shared mental model amongst teammates. In the case of incongruous models, catastrophic failures may occur unless mitigating steps are taken. To identify and remedy these potential issues, we propose a novel mechanism for enabling an autonomous system to detect model disparity between itself and a human collaborator, infer the source of the disagreement within the model, evaluate potential consequences of this error, and finally, provide human-interpretable feedback to encourage model correction. This process effectively enables a robot to provide a human with a policy update based on perceived model disparity, reducing the likelihood of costly or dangerous failures during joint task execution. This paper makes two contributions at the intersection of explainable AI (xAI) and human-robot collaboration: 1) The Reward Augmentation and Repair through Explanation (RARE) framework for estimating task understanding and 2) A human subjects study illustrating the effectiveness of reward augmentation-based policy repair in a complex collaborative task. [ABSTRACT FROM AUTHOR]

Published
2019

6. Formulation, Development and Characterization of Ethosomal Gel of Atorvastatin.

Author

Agrawal, Shivendra and Gautam, Girendra Kumar

Subjects
*DRUG solubility, *LECITHIN, *ATORVASTATIN, *CONGESTIVE heart failure, *COLLOIDS, *DRUG bioavailability, *REDUCTASE inhibitors
Abstract

The current study was conducted to develop vesicular ethosomal gel system for antihypertensive drug atorvastatin. Atorvastatin, aHMG-CoA reductase inhibitor used in the treatment of congestive heart failure (CHF). Atorvastatin was formulated in to ethosomal gel formulation to reduce the first pass metabolism and increase the systemic bioavailability. Ethosomes composed of phospholipids oya lecithin, ethanol and cholesterol were prepared by cold method followed by ultrasonicat ion. The atorvastatin ethosomes were then characterized for their particle size, zeta potential followed by invitro drug release profile. The optimized ethosoml formulation was then incorporated in to gellinga gent, carbopol 934 to prepare ethosomal gel formulation. The ethosomal gel formulat ion was then subjected to physicochemical characterization, spreadibility and invitro drug release profile. The ethosomal gel formulation showed 15.69g. cm spreadibility and 98.13% invitro drug release with in 48 hours as compared to plan drug ethosomal formulation which shows 52.37 % in 48 hours. Hence ethosomal gel delivery system provide a good design for topical delivery of drug with enhanced bioavailability and patient compliance. [ABSTRACT FROM AUTHOR]

Published
2020

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