Joydeep Biswas

About

My ultimate goal is to have self-sufficient autonomous mobile robots working in human environments, performing tasks accurately and robustly. In support of this goal, I am interested in research in perception, planning, and control applied to autonomous mobile robots. My research in perception involves developing models and representations for a dynamic world, and algorithms to build and perform inference based on such models. My interests in planning include motion planning, multi-robot coordination, and task-based planning in domains including service mobile robots, and robot soccer.

Research topics

• Computer Science
• Artificial Intelligence
• Mathematics
• Cartography
• Geography
• Operations research
• Real-time computing
• Simulation
• Programming language
• Data science

Selected publications

• Socially CompliAnt Navigation Dataset (SCAND): A Large-Scale Dataset of Demonstrations for Social Navigation

  IEEE Robotics and Automation Letters · 2022 · 100 citations

  Social navigation is the capability of an autonomous agent, such as a robot, to navigate in a “socially compliant” manner in the presence of other intelligent agents such as humans. With the emergence of autonomously navigating mobile robots in human-populated environments (e.g., domestic service robots in homes and restaurants and food delivery robots on public sidewalks), incorporating socially compliant navigation behaviors on these robots becomes critical to ensuring safe and comfortable hum…

  DOI

• LLM+P: Empowering Large Language Models with Optimal Planning Proficiency

  arXiv (Cornell University) · 2023 · 84 citations

  Large language models (LLMs) have demonstrated remarkable zero-shot generalization abilities: state-of-the-art chatbots can provide plausible answers to many common questions that arise in daily life. However, so far, LLMs cannot reliably solve long-horizon planning problems. By contrast, classical planners, once a problem is given in a formatted way, can use efficient search algorithms to quickly identify correct, or even optimal, plans. In an effort to get the best of both worlds, this paper i…

  DOI

• Learning Inverse Kinodynamics for Accurate High-Speed Off-Road Navigation on Unstructured Terrain

  IEEE Robotics and Automation Letters · 2021 · 61 citations

  This letter presents a learning-based approach to consider the effect of unobservable world states in kinodynamic motion planning in order to enable accurate high-speed off-road navigation on unstructured terrain. Existing kinodynamic motion planners either operate in structured and homogeneous environments and thus do not need to explicitly account for terrain-vehicle interaction, or assume a set of discrete terrain classes. However, when operating on unstructured terrain, especially at high sp…

  DOI

• Deadlock-free, safe, and decentralized multi-robot navigation in social mini-games via discrete-time control barrier functions

  Autonomous Robots · 2025-04-20 · 6 citations

  articleOpen accessSenior author

  Abstract We present an approach to ensure safe and deadlock-free navigation for decentralized multi-robot systems operating in constrained environments, including doorways and intersections. Although many solutions have been proposed that ensure safety and resolve deadlocks, optimally preventing deadlocks in a minimally invasive and decentralized fashion remains an open problem. We first formalize the objective as a non-cooperative, non-communicative, partially observable multi-robot navigation…

  PublisherOA PDFDOI

• The Essentials of AI for Life and Society: An AI Literacy Course for the University Community

  Proceedings of the AAAI Conference on Artificial Intelligence · 2025-04-11 · 2 citations

  articleOpen access1st authorCorresponding

  We describe the development of a one-credit course to promote AI literacy at the University of Texas at Austin. In response to a call for the rapid deployment of class that would serve a broad audience in Fall of 2023, we designed a 14-week seminar-style course that incorporated an interdisciplinary group of speakers who lectured on topics ranging from the fundamentals of AI to societal concerns including disinformation and employment. University students, faculty, and staff, and even community…

  PublisherOA PDFDOI

Recent grants

• CAREER: Robust Perception and Customization for Long-Term Autonomous Mobile Service Robots

  NSF · $590k · 2021–2027

• Collaborative Research: RI: Medium: Introspective Perception and Planning for Long-Term Autonomy

  NSF · $600k · 2020–2024

• Collaborative Research: SHF: Small: Interactive Synthesis and Repair For Robot Programs

  NSF · $250k · 2020–2023

Frequent coauthors

• Sadegh Rabiee

  The University of Texas at Austin

  28 shared

• Peter Stone

  23 shared

• Manuela Veloso

  23 shared

• Arjun Guha

  Northeastern University

  19 shared

• Jarrett Holtz

  Robert Bosch (United States)

  18 shared

• Haresh Karnan

  17 shared

• Xuesu Xiao

  16 shared

• Garrett Warnell

  15 shared

Labs

• Autonomous Mobile Robotics Laboratory (AMRL)PI

  Research in perception, planning, and control applied to autonomous mobile robots

Education

• PhD, Robotics Institute

  Carnegie Mellon University

  2014

Awards & honors

• NSF Research Traineeship Award
• NSF CAREER Award
• NSF Award
• J.P. Morgan Faculty Research Award
• Amazon Research Award

Similar researchers at University of Texas at Austin

• Aditya Akellah-60
• Chandrajit Bajajh-56
• Scott Aaronsonh-51