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Reza Ghanadan

7 accepted papers

2025

Is the House Ready For Sleeptime? Generating and Evaluating Situational Queries for Embodied Question Answering

Vishnu Sashank Dorbala, Prasoon Goyal, Robinson Piramuthu, Michael Johnston, Reza Ghanadan, Dinesh Manocha
IROS 2025

We present and tackle the problem of Embodied Question Answering (EQA) with Situational Queries (S-EQA) in a household environment. Unlike prior EQA work tackling simple queries that directly reference target objects and properties ("What is the color of the car?"), situational queries (such as "Is

Cited by 2Source ↗Scholar ↗
2024

"Don't Forget to Put the Milk Back!" Dataset for Enabling Embodied Agents to Detect Anomalous Situations

James F. Mullen Jr., Prasoon Goyal, Robinson Piramuthu, Michael Johnston, Dinesh Manocha, Reza Ghanadan
RA-L 2024

Home robots intend to make their users lives easier. Our work moves toward more helpful home robots by enabling them to inform their users of dangerous or unsanitary anomalies in the home. Some examples of these anomalies include the user leaving their milk out, forgetting to turn off the stove, or

Cited by 13Source ↗Scholar ↗
2024

MICo: Preventative Detoxification of Large Language Models through Inhibition Control

Roy Siegelmann, Ninareh Mehrabi, Palash Goyal, Prasoon Goyal, Lisa Bauer, Jwala Dhamala +3
NAACL 2024findings

Large Language Models (LLMs) are powerful tools which have been both dominant and commonplace in the field of Artificial Intelligence. Yet, LLMs have a tendency to devolve into toxic degeneration, wherein otherwise safe and unproblematic models begin generating toxic content. For the sake of social…

Cited by 2Source ↗PDF ↗Scholar ↗
2024

Mastering Robot Manipulation with Multimodal Prompts through Pretraining and Multi-task Fine-tuning

Jiachen Li, Qiaozi Gao, Michael Johnston, Xiaofeng Gao, Xuehai He, Hangjie Shi +3
ICML 2024poster

Prompt-based learning has been demonstrated as a compelling paradigm contributing to large language models' tremendous success (LLMs). Inspired by their success in language tasks, existing research has leveraged LLMs in embodied instruction following and task planning. In this work, we tackle the pr…

Cited by 11Source ↗PDF ↗Scholar ↗
2024

X-Eval: Generalizable Multi-aspect Text Evaluation via Augmented Instruction Tuning with Auxiliary Evaluation Aspects

Minqian Liu, Ying Shen, Zhiyang Xu, Yixin Cao, Eunah Cho, Vaibhav Kumar +2
NAACL 2024long

Natural Language Generation (NLG) typically involves evaluating the generated text in various aspects (e.g., consistency and naturalness) to obtain a comprehensive assessment. However, multi-aspect evaluation remains challenging as it may require the evaluator to generalize to any given evaluation a…

Cited by 26Source ↗PDF ↗code ↗Scholar ↗
2023

Alexa Arena: A User-Centric Interactive Platform for Embodied AI

Qiaozi Gao, Govind Thattai, Suhaila Shakiah, Xiaofeng Gao, Shreyas Pansare, Vasu Sharma +19
NeurIPS 2023poster

We introduce Alexa Arena, a user-centric simulation platform to facilitate research in building assistive conversational embodied agents. Alexa Arena features multi-room layouts and an abundance of interactable objects. With user-friendly graphics and control mechanisms, the platform supports the de…

Cited by 32Source ↗PDF ↗code ↗Scholar ↗
2020

Assisted Learning: A Framework for Multi-Organization Learning

Xun Xian, Xinran Wang, Jie Ding, Reza Ghanadan
NeurIPS 2020spotlight

In an increasing number of AI scenarios, collaborations among different organizations or agents (e.g., human and robots, mobile units) are often essential to accomplish an organization-specific mission. However, to avoid leaking useful and possibly proprietary information, organizations typically en…

Cited by 53Source ↗PDF ↗Scholar ↗