Search Results for author: Joydeep Biswas

Found 40 papers, 12 papers with code

PhD Knowledge Not Required: A Reasoning Challenge for Large Language Models

1 code implementation3 Feb 2025 Carolyn Jane Anderson, Joydeep Biswas, Aleksander Boruch-Gruszecki, Federico Cassano, Molly Q Feldman, Arjun Guha, Francesca Lucchetti, Zixuan Wu

We also quantify the effectiveness of reasoning longer with R1 and Gemini Thinking to identify the point beyond which more reasoning is unlikely to improve accuracy on our benchmark.

General Knowledge

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

no code implementations13 Jan 2025 Joydeep Biswas, Don Fussell, Peter Stone, Kristin Patterson, Kristen Procko, Lea Sabatini, Zifan Xu

We describe the development of a one-credit course to promote AI literacy at The University of Texas at Austin.

Learning Quantitative Automata Modulo Theories

no code implementations15 Nov 2024 Eric Hsiung, Swarat Chaudhuri, Joydeep Biswas

Quantitative automata are useful representations for numerous applications, including modeling probability distributions over sequences to Markov chains and reward machines.

Active Learning valid

PACER: Preference-conditioned All-terrain Costmap Generation

no code implementations30 Oct 2024 Luisa Mao, Garrett Warnell, Peter Stone, Joydeep Biswas

In autonomous robot navigation, terrain cost assignment is typically performed using a semantics-based paradigm in which terrain is first labeled using a pre-trained semantic classifier and costs are then assigned according to a user-defined mapping between label and cost.

Representation Learning Robot Navigation

Creating and Repairing Robot Programs in Open-World Domains

no code implementations24 Oct 2024 Claire Schlesinger, Arjun Guha, Joydeep Biswas

Using Large Language Models (LLMs) to produce robot programs from natural language has allowed for robot systems that can complete a higher diversity of tasks.

Diversity

Joint Verification and Refinement of Language Models for Safety-Constrained Planning

no code implementations18 Oct 2024 Yunhao Yang, William Ward, Zichao Hu, Joydeep Biswas, Ufuk Topcu

Given a high-level task description in natural language, the proposed method queries a language model to generate plans in the form of executable robot programs.

Language Modeling Language Modelling

ReMEmbR: Building and Reasoning Over Long-Horizon Spatio-Temporal Memory for Robot Navigation

1 code implementation20 Sep 2024 Abrar Anwar, John Welsh, Joydeep Biswas, Soha Pouya, Yan Chang

To address this problem, we introduce a Retrieval-augmented Memory for Embodied Robots, or ReMEmbR, a system designed for long-horizon video question answering for robot navigation.

Descriptive Question Answering +2

CLOVER: Context-aware Long-term Object Viewpoint- and Environment- Invariant Representation Learning

no code implementations12 Jul 2024 Dongmyeong Lee, Amanda Adkins, Joydeep Biswas

In many applications, robots can benefit from object-level understanding of their environments, including the ability to distinguish object instances and re-identify previously seen instances.

Foreground Segmentation Object +1

Lift, Splat, Map: Lifting Foundation Masks for Label-Free Semantic Scene Completion

no code implementations3 Jul 2024 Arthur Zhang, Rainier Heijne, Joydeep Biswas

To address these limitations, we propose LSMap, a method that lifts masks from visual foundation models to predict a continuous, open-set semantic and elevation-aware representation in bird's eye view (BEV) for the entire scene, including regions underneath dynamic entities and in occluded areas.

Representation Learning

Robo-Instruct: Simulator-Augmented Instruction Alignment For Finetuning CodeLLMs

no code implementations30 May 2024 Zichao Hu, Junyi Jessy Li, Arjun Guha, Joydeep Biswas

In this work, we introduce ROBO-INSTRUCT that preserves the diversity of programs generated by an LLM while providing the correctness of simulator-based checking.

Diversity

Multi-Agent Inverse Reinforcement Learning in Real World Unstructured Pedestrian Crowds

no code implementations26 May 2024 Rohan Chandra, Haresh Karnan, Negar Mehr, Peter Stone, Joydeep Biswas

In this paper, we present a new multi-agent maximum entropy inverse reinforcement learning algorithm for real world unstructured pedestrian crowds.

Imitation Learning Motion Planning +2

Dynamic Model Predictive Shielding for Provably Safe Reinforcement Learning

no code implementations22 May 2024 Arko Banerjee, Kia Rahmani, Joydeep Biswas, Isil Dillig

When planning recovery actions for ensuring safety, the planner utilizes the neural policy to estimate long-term rewards, allowing it to observe beyond its short-term planning horizon.

reinforcement-learning Reinforcement Learning +1

SYNAPSE: SYmbolic Neural-Aided Preference Synthesis Engine

no code implementations25 Mar 2024 Sadanand Modak, Noah Patton, Isil Dillig, Joydeep Biswas

We address this problem using a novel framework called SYNAPSE, which is a neuro-symbolic approach designed to efficiently learn preferential concepts from limited data.

Autonomous Driving Out-of-Distribution Generalization +1

Looking Inside Out: Anticipating Driver Intent From Videos

1 code implementation3 Dec 2023 Yung-chi Kung, Arthur Zhang, Junmin Wang, Joydeep Biswas

In this work, we propose a novel method of utilizing in-cabin and external camera data to improve state-of-the-art (SOTA) performance in predicting future driver actions.

ObVi-SLAM: Long-Term Object-Visual SLAM

no code implementations26 Sep 2023 Amanda Adkins, Taijing Chen, Joydeep Biswas

Robots responsible for tasks over long time scales must be able to localize consistently and scalably amid geometric, viewpoint, and appearance changes.

Object Visual Odometry

STERLING: Self-Supervised Terrain Representation Learning from Unconstrained Robot Experience

no code implementations26 Sep 2023 Haresh Karnan, Elvin Yang, Daniel Farkash, Garrett Warnell, Joydeep Biswas, Peter Stone

Terrain awareness, i. e., the ability to identify and distinguish different types of terrain, is a critical ability that robots must have to succeed at autonomous off-road navigation.

Representation Learning Visual Navigation

Wait, That Feels Familiar: Learning to Extrapolate Human Preferences for Preference Aligned Path Planning

no code implementations18 Sep 2023 Haresh Karnan, Elvin Yang, Garrett Warnell, Joydeep Biswas, Peter Stone

In this work, we posit that operator preferences for visually novel terrains, which the robot should adhere to, can often be extrapolated from established terrain references within the inertial, proprioceptive, and tactile domain.

Navigate Robot Navigation +1

Automata Learning from Preference and Equivalence Queries

no code implementations18 Aug 2023 Eric Hsiung, Joydeep Biswas, Swarat Chaudhuri

Active automata learning from membership and equivalence queries is a foundational problem with numerous applications.

Navigate

Decentralized Social Navigation with Non-Cooperative Robots via Bi-Level Optimization

1 code implementation15 Jun 2023 Rohan Chandra, Rahul Menon, Zayne Sprague, Arya Anantula, Joydeep Biswas

This paper presents a fully decentralized approach for realtime non-cooperative multi-robot navigation in social mini-games, such as navigating through a narrow doorway or negotiating right of way at a corridor intersection.

Collision Avoidance Multi-agent Reinforcement Learning +3

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

1 code implementation22 Apr 2023 Bo Liu, Yuqian Jiang, Xiaohan Zhang, Qiang Liu, Shiqi Zhang, Joydeep Biswas, Peter Stone

LLM+P takes in a natural language description of a planning problem, then returns a correct (or optimal) plan for solving that problem in natural language.

Zero-shot Generalization

System Configuration and Navigation of a Guide Dog Robot: Toward Animal Guide Dog-Level Guiding Work

no code implementations24 Oct 2022 Hochul Hwang, Tim Xia, Ibrahima Keita, Ken Suzuki, Joydeep Biswas, Sunghoon I. Lee, Donghyun Kim

A robot guide dog has compelling advantages over animal guide dogs for its cost-effectiveness, potential for mass production, and low maintenance burden.

SOCIALMAPF: Optimal and Efficient Multi-Agent Path Finding with Strategic Agents for Social Navigation

no code implementations15 Oct 2022 Rohan Chandra, Rahul Maligi, Arya Anantula, Joydeep Biswas

We perform an extensive array of experiments that show that optimal search-based MAPF techniques lead to collisions and increased time-to-goal in SocialMAPF compared to our proposed method using mechanism design.

Motion Planning Multi-Agent Path Finding +1

High-Speed Accurate Robot Control using Learned Forward Kinodynamics and Non-linear Least Squares Optimization

no code implementations16 Jun 2022 Pranav Atreya, Haresh Karnan, Kavan Singh Sikand, Xuesu Xiao, Sadegh Rabiee, Joydeep Biswas

However, the types of control problems these approaches can be applied to are limited only to that of following pre-computed kinodynamically feasible trajectories.

Dense Crowd Flow-Informed Path Planning

no code implementations1 Jun 2022 Emily Pruc, Shlomo Zilberstein, Joydeep Biswas

In the case of pedestrian-unaware mobile robots this desire for safety leads to the freezing robot problem, where a robot confronted with a large dynamic group of obstacles (such as a crowd of pedestrians) would determine all forward navigation unsafe causing the robot to stop in place.

Navigate

VI-IKD: High-Speed Accurate Off-Road Navigation using Learned Visual-Inertial Inverse Kinodynamics

no code implementations30 Mar 2022 Haresh Karnan, Kavan Singh Sikand, Pranav Atreya, Sadegh Rabiee, Xuesu Xiao, Garrett Warnell, Peter Stone, Joydeep Biswas

In this paper, we hypothesize that to enable accurate high-speed off-road navigation using a learned IKD model, in addition to inertial information from the past, one must also anticipate the kinodynamic interactions of the vehicle with the terrain in the future.

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

no code implementations28 Mar 2022 Haresh Karnan, Anirudh Nair, Xuesu Xiao, Garrett Warnell, Soeren Pirk, Alexander Toshev, Justin Hart, Joydeep Biswas, Peter Stone

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.

Imitation Learning Navigate +1

STEADY: Simultaneous State Estimation and Dynamics Learning from Indirect Observations

1 code implementation2 Mar 2022 Jiayi Wei, Jarrett Holtz, Isil Dillig, Joydeep Biswas

Accurate kinodynamic models play a crucial role in many robotics applications such as off-road navigation and high-speed driving.

Competence-Aware Path Planning via Introspective Perception

no code implementations28 Sep 2021 Sadegh Rabiee, Connor Basich, Kyle Hollins Wray, Shlomo Zilberstein, Joydeep Biswas

First, perception errors are learned in a model-free and location-agnostic setting via introspective perception prior to deployment in novel environments.

Visual Representation Learning for Preference-Aware Path Planning

no code implementations18 Sep 2021 Kavan Singh Sikand, Sadegh Rabiee, Adam Uccello, Xuesu Xiao, Garrett Warnell, Joydeep Biswas

We introduce Visual Representation Learning for Preference-Aware Path Planning (VRL-PAP), an alternative approach that overcomes all three limitations: VRL-PAP leverages unlabeled human demonstrations of navigation to autonomously generate triplets for learning visual representations of terrain that are viewpoint invariant and encode terrain types in a continuous representation space.

Representation Learning Semantic Segmentation

Iterative Program Synthesis for Adaptable Social Navigation

1 code implementation8 Mar 2021 Jarrett Holtz, Simon Andrews, Arjun Guha, Joydeep Biswas

Robot social navigation is influenced by human preferences and environment-specific scenarios such as elevators and doors, thus necessitating end-user adaptability.

Program Synthesis Robotics Programming Languages

Robot Action Selection Learning via Layered Dimension Informed Program Synthesis

1 code implementation10 Aug 2020 Jarrett Holtz, Arjun Guha, Joydeep Biswas

Action selection policies (ASPs), used to compose low-level robot skills into complex high-level tasks are commonly represented as neural networks (NNs) in the state of the art.

Autonomous Driving Program Repair

IV-SLAM: Introspective Vision for Simultaneous Localization and Mapping

no code implementations6 Aug 2020 Sadegh Rabiee, Joydeep Biswas

Existing solutions to visual simultaneous localization and mapping (V-SLAM) assume that errors in feature extraction and matching are independent and identically distributed (i. i. d), but this assumption is known to not be true -- features extracted from low-contrast regions of images exhibit wider error distributions than features from sharp corners.

Simultaneous Localization and Mapping

Learning to Optimize Autonomy in Competence-Aware Systems

no code implementations17 Mar 2020 Connor Basich, Justin Svegliato, Kyle Hollins Wray, Stefan Witwicki, Joydeep Biswas, Shlomo Zilberstein

Interest in semi-autonomous systems (SAS) is growing rapidly as a paradigm to deploy autonomous systems in domains that require occasional reliance on humans.

Autonomous Driving

Localization under Topological Uncertainty for Lane Identification of Autonomous Vehicles

no code implementations4 Mar 2018 Samer B. Nashed, David M. Ilstrup, Joydeep Biswas

We present the Variable Structure Multiple Hidden Markov Model (VSM-HMM) as a framework for localizing in the presence of topological uncertainty, and demonstrate its effectiveness on an AV where lane membership is modeled as a topological localization process.

Autonomous Vehicles Decision Making

Interactive Robot Transition Repair With SMT

2 code implementations5 Feb 2018 Jarrett Holtz, Arjun Guha, Joydeep Biswas

Complex robot behaviors are often structured as state machines, where states encapsulate actions and a transition function switches between states.

Robotics Programming Languages

Human-in-the-Loop SLAM

1 code implementation23 Nov 2017 Samer B. Nashed, Joydeep Biswas

Building large-scale, globally consistent maps is a challenging problem, made more difficult in environments with limited access, sparse features, or when using data collected by novice users.

Human-Computer Interaction Robotics

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