no code implementations • 19 Mar 2024 • Rui Liu, Amisha Bhaskar, Pratap Tokekar
Notably, our model, trained solely on data from a transparent glass bowl containing granular cereals, showcases generalization ability when tested zero-shot on other bowl configurations with different types of food.
no code implementations • 13 Mar 2024 • Peihong Yu, Manav Mishra, Alec Koppel, Carl Busart, Priya Narayan, Dinesh Manocha, Amrit Bedi, Pratap Tokekar
Multi-Agent Reinforcement Learning (MARL) algorithms face the challenge of efficient exploration due to the exponential increase in the size of the joint state-action space.
no code implementations • 13 Mar 2024 • Rui Liu, Erfaun Noorani, Pratap Tokekar, John S. Baras
In this study, we conduct a thorough iteration complexity analysis for the risk-sensitive policy gradient method, focusing on the REINFORCE algorithm and employing the exponential utility function.
no code implementations • 22 Dec 2023 • Souradip Chakraborty, Anukriti Singh, Amisha Bhaskar, Pratap Tokekar, Dinesh Manocha, Amrit Singh Bedi
Current methods to mitigate this misalignment work by learning reward functions from human preferences; however, they inadvertently introduce a risk of reward overoptimization.
no code implementations • 8 Nov 2023 • Peihong Yu, Bhoram Lee, Aswin Raghavan, Supun Samarasekara, Pratap Tokekar, James Zachary Hare
Our results demonstrate the efficacy of COP integration, and show that COP-based training leads to robust policies compared to state-of-the-art Multi-Agent Reinforcement Learning (MARL) methods when faced with out-of-distribution initial states.
no code implementations • 10 Oct 2023 • Vishnu Dutt Sharma, Anukriti Singh, Pratap Tokekar
2D top-down maps are commonly used for the navigation and exploration of mobile robots through unknown areas.
no code implementations • 14 Mar 2023 • Souradip Chakraborty, Kasun Weerakoon, Prithvi Poddar, Mohamed Elnoor, Priya Narayanan, Carl Busart, Pratap Tokekar, Amrit Singh Bedi, Dinesh Manocha
Reinforcement learning-based policies for continuous control robotic navigation tasks often fail to adapt to changes in the environment during real-time deployment, which may result in catastrophic failures.
no code implementations • 2 Mar 2023 • Guangyao Shi, Pratap Tokekar
We study the problem of learning a function that maps context observations (input) to parameters of a submodular function (output).
no code implementations • 30 Nov 2022 • Troi Williams, Po-Lun Chen, Sparsh Bhogavilli, Vaibhav Sanjay, Pratap Tokekar
To find an optimal sensor state that minimizes the rover's localization uncertainty, DyFOS uses a localization uncertainty prediction pipeline in an optimization search.
no code implementations • 9 Nov 2022 • Vishnu Dutt Sharma, John P. Dickerson, Pratap Tokekar
Green Security Games with real-time information (GSG-I) add the real-time information about the agents' movement to the typical GSG formulation.
no code implementations • 12 Jun 2022 • Souradip Chakraborty, Amrit Singh Bedi, Alec Koppel, Pratap Tokekar, Dinesh Manocha
In this paper, we present a novel Heavy-Tailed Stochastic Policy Gradient (HT-PSG) algorithm to deal with the challenges of sparse rewards in continuous control problems.
no code implementations • 2 Jun 2022 • Souradip Chakraborty, Amrit Singh Bedi, Alec Koppel, Brian M. Sadler, Furong Huang, Pratap Tokekar, Dinesh Manocha
Model-based approaches to reinforcement learning (MBRL) exhibit favorable performance in practice, but their theoretical guarantees in large spaces are mostly restricted to the setting when transition model is Gaussian or Lipschitz, and demands a posterior estimate whose representational complexity grows unbounded with time.
no code implementations • 28 Jan 2022 • Amrit Singh Bedi, Souradip Chakraborty, Anjaly Parayil, Brian Sadler, Pratap Tokekar, Alec Koppel
Doing so incurs a persistent bias that appears in the attenuation rate of the expected policy gradient norm, which is inversely proportional to the radius of the action space.
no code implementations • ICLR 2022 • Zhi Zhang, Zhuoran Yang, Han Liu, Pratap Tokekar, Furong Huang
This paper proposes a new algorithm for learning the optimal policies under a novel multi-agent predictive state representation reinforcement learning model.
1 code implementation • 18 May 2021 • Lifeng Zhou, Vishnu D. Sharma, QingBiao Li, Amanda Prorok, Alejandro Ribeiro, Pratap Tokekar, Vijay Kumar
We demonstrate the performance of our GNN-based learning approach in a scenario of active target tracking with large networks of robots.
no code implementations • 13 Jan 2021 • Deniz Ozsoyeller, Pratap Tokekar
We study the Symmetric Rendezvous Search Problem for a multi-robot system.
Robotics Discrete Mathematics
1 code implementation • 2 Nov 2020 • Jingxi Chen, Amrish Baskaran, Zhongshun Zhang, Pratap Tokekar
Specifically, we present a Multi-Agent Graph Attention Proximal Policy Optimization (MA-G-PPO) algorithm that takes as input the local observations of all agents combined with a low resolution global map to learn a policy for each agent.
1 code implementation • 7 Jul 2020 • Kevin Yu, Jason M. O'Kane, Pratap Tokekar
The goal is to find a coordinated strategy for the UAV and UGV that visits and covers all cells in minimum time, while optimally finding how much to recharge, where to recharge, and when to recharge the battery.
Robotics
no code implementations • 15 Apr 2020 • Aravind Preshant Premkumar, Kevin Yu, Pratap Tokekar
We present an algorithm to explore an orthogonal polygon using a team of $p$ robots.
no code implementations • 25 Mar 2020 • Vishnu D. Sharma, Maymoonah Toubeh, Lifeng Zhou, Pratap Tokekar
Deep learning techniques can be used for semantic segmentation of the aerial image to create a cost map for safe ground robot navigation.
Robotics
no code implementations • 13 Mar 2020 • Deeksha Dixit, Surabhi Verma, Pratap Tokekar
We evaluate the performance of this method using a city-wide dataset collected in a photorealistic simulation by varying four parameters: height of aerial images, the pitch of the aerial camera mount, FOV of the ground camera, and the methodology of fusing cross-view measurements in the particle filter.
1 code implementation • 30 Oct 2019 • Harnaik Dhami, Kevin Yu, Tianshu Xu, Qian Zhu, Kshitiz Dhakal, James Friel, Song Li, Pratap Tokekar
We also present a toolchain that can be used to create phenotyping farms for use in Gazebo simulations.
1 code implementation • 3 Oct 2019 • Kevin Yu, Prajwal Shanthakumar, Jonah Orevillo, Eric Bianchi, Matthew Hebdon, Pratap Tokekar
With local navigation routines, a supervisor, and a planner we construct a system that can fully and autonomously inspect box girder bridges when standard methods are unavailable.
Robotics
no code implementations • 2 Oct 2019 • Lifeng Zhou, Vasileios Tzoumas, George J. Pappas, Pratap Tokekar
Since, DRM overestimates the number of attacks in each clique, in this paper we also introduce an Improved Distributed Robust Maximization (IDRM) algorithm.
no code implementations • 13 Sep 2019 • Maymoonah Toubeh, Pratap Tokekar
Images taken from the aerial view are used to provide a less obstructed map to guide the navigation of the robot on the ground.
no code implementations • 24 Jul 2018 • Lifeng Zhou, Pratap Tokekar
We formulate a discrete submodular maximization problem for selecting a set using Conditional-Value-at-Risk (CVaR), a risk metric commonly used in financial analysis.
1 code implementation • 31 Mar 2017 • Kevin Yu, Ashish Kumar Budhiraja, Pratap Tokekar
We envision scenarios where the UAV can be recharged along the way either by landing on stationary recharging stations or on Unmanned Ground Vehicles (UGVs) acting as mobile recharging stations.
Robotics Multiagent Systems
no code implementations • 31 Aug 2016 • Gordon Christie, Adam Shoemaker, Kevin Kochersberger, Pratap Tokekar, Lance McLean, Alexander Leonessa
Autonomously searching for hazardous radiation sources requires the ability of the aerial and ground systems to understand the scene they are scouting.