Sadanand Modak — accepted AI-conference papers

2025

ConfigBot: Adaptive Resource Allocation for Robot Applications in Dynamic Environments

Rohit Dwivedula, Sadanand Modak, Aditya Akella, Joydeep Biswas, Daehyeok Kim, Christopher J. Rossbach

IROS 2025

The growing use of service robots in dynamic environments requires flexible management of on-board compute resources to optimize the performance of diverse tasks such as navigation, localization, and perception. Current robot deployments often rely on static OS configurations and system over-provisi

Cited by 1 Source ↗code ↗Scholar ↗

2025

SYNAPSE: SYmbolic Neural-Aided Preference Synthesis Engine

Sadanand Modak, Noah Tobias Patton, Isil Dillig, Joydeep Biswas

AAAI 2025technical

This paper addresses the problem of preference learning, which aims to align robot behaviors through learning user-specific preferences (e.g. “good pull-over location”) from visual demonstrations. Despite its similarity to learning factual concepts (e.g. “red door”), preference learning is a fundame…

Cited by 0 Source ↗PDF ↗Scholar ↗

2024

Deploying and Evaluating LLMs to Program Service Mobile Robots

Zichao Hu, Francesca Lucchetti, Claire Schlesinger, Yash Saxena, Anders Freeman, Sadanand Modak +2

RA-L 2024

Recent advancements in large language models (LLMs) have spurred interest in using them for generating robot programs from natural language, with promising initial results. We investigate the use of LLMs to generate programs for service mobile robots leveraging mobility, perception, and human intera

Cited by 60 Source ↗code ↗Scholar ↗