Prasoon Goyal — accepted AI-conference 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 2 Source ↗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 13 Source ↗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 2 Source ↗PDF ↗Scholar ↗

2020

PixL2R: Guiding Reinforcement Learning Using Natural Language by Mapping Pixels to Rewards

Prasoon Goyal, Scott Niekum, Raymond Mooney

CoRL 2020

Reinforcement learning (RL), particularly in sparse reward settings, often requires prohibitively large numbers of interactions with the environment, thereby limiting its applicability to complex problems. To address this, several prior approaches have used natural language to guide the agent’s expl

Cited by 0 Source ↗PDF ↗code ↗Scholar ↗

2017

Nonparametric Variational Auto-Encoders for Hierarchical Representation Learning

Prasoon Goyal, Zhiting Hu, Xiaodan Liang, Chenyu Wang, Eric P. Xing

ICCV 2017poster

The recently developed variational autoencoders (VAEs) have proved to be an effective confluence of the rich representational power of neural networks with Bayesian methods. However, most work on VAEs use a rather simple prior over the latent variables such as standard normal distribution, thereby r…

Cited by 137 PDF ↗Scholar ↗