1 code implementation • 3 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.
no code implementations • 13 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.
no code implementations • 15 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.
no code implementations • 30 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.
no code implementations • 24 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.
no code implementations • 18 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.
1 code implementation • 20 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.
no code implementations • 12 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.
no code implementations • 3 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.
no code implementations • 30 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.
no code implementations • 26 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.
no code implementations • 22 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.
no code implementations • 25 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.
no code implementations • 13 Dec 2023 • Divyanshu Saxena, Nihal Sharma, Donghyun Kim, Rohit Dwivedula, Jiayi Chen, Chenxi Yang, Sriram Ravula, Zichao Hu, Aditya Akella, Sebastian Angel, Joydeep Biswas, Swarat Chaudhuri, Isil Dillig, Alex Dimakis, P. Brighten Godfrey, Daehyeok Kim, Chris Rossbach, Gang Wang
This paper lays down the research agenda for a domain-specific foundation model for operating systems (OSes).
1 code implementation • 3 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.
no code implementations • 26 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.
no code implementations • 26 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.
2 code implementations • 24 Sep 2023 • Arthur Zhang, Chaitanya Eranki, Christina Zhang, Ji-Hwan Park, Raymond Hong, Pranav Kalyani, Lochana Kalyanaraman, Arsh Gamare, Arnav Bagad, Maria Esteva, Joydeep Biswas
Using our dataset and annotations, we release benchmarks for 3D object detection and 3D semantic segmentation using established metrics.
no code implementations • 18 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.
no code implementations • 18 Aug 2023 • Eric Hsiung, Joydeep Biswas, Swarat Chaudhuri
Active automata learning from membership and equivalence queries is a foundational problem with numerous applications.
no code implementations • 29 Jun 2023 • Anthony Francis, Claudia Pérez-D'Arpino, Chengshu Li, Fei Xia, Alexandre Alahi, Rachid Alami, Aniket Bera, Abhijat Biswas, Joydeep Biswas, Rohan Chandra, Hao-Tien Lewis Chiang, Michael Everett, Sehoon Ha, Justin Hart, Jonathan P. How, Haresh Karnan, Tsang-Wei Edward Lee, Luis J. Manso, Reuth Mirksy, Sören Pirk, Phani Teja Singamaneni, Peter Stone, Ada V. Taylor, Peter Trautman, Nathan Tsoi, Marynel Vázquez, Xuesu Xiao, Peng Xu, Naoki Yokoyama, Alexander Toshev, Roberto Martín-Martín
A major challenge to deploying robots widely is navigation in human-populated environments, commonly referred to as social robot navigation.
1 code implementation • 15 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.
1 code implementation • 22 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.
1 code implementation • 9 Mar 2023 • Zayne Sprague, Rohan Chandra, Jarrett Holtz, Joydeep Biswas
We present SocialGym 2, a multi-agent navigation simulator for social robot research.
no code implementations • 24 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.
no code implementations • 15 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.
no code implementations • 16 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.
no code implementations • 1 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.
no code implementations • 30 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.
no code implementations • 28 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.
1 code implementation • 2 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.
no code implementations • 28 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.
no code implementations • 18 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.
1 code implementation • 8 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
1 code implementation • 10 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.
no code implementations • 6 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.
no code implementations • 17 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.
no code implementations • 4 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.
2 code implementations • 5 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
1 code implementation • 23 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