Search Results for author: Pratap Tokekar

Found 22 papers, 6 papers with code

RE-MOVE: An Adaptive Policy Design Approach for Dynamic Environments via Language-Based Feedback

no code implementations14 Mar 2023 Souradip Chakraborty, Kasun Weerakoon, Prithvi Poddar, 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.

Continuous Control

Decision-Oriented Learning with Differentiable Submodular Maximization for Vehicle Routing Problem

no code implementations2 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).

Dynamically Finding Optimal Observer States to Minimize Localization Error with Complex State-Dependent Noise

no code implementations30 Nov 2022 Troi Williams, Po-Lun Chen, Sparsh Bhogavilli, Vaibhav Sanjay, Pratap Tokekar

The output of the optimization search is an optimal observer state that minimizes target localization uncertainty while avoiding occlusion and collision.

Interpretable Deep Reinforcement Learning for Green Security Games with Real-Time Information

no code implementations9 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.

Decision Making reinforcement-learning +1

Dealing with Sparse Rewards in Continuous Control Robotics via Heavy-Tailed Policies

no code implementations12 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.

Continuous Control OpenAI Gym

Posterior Coreset Construction with Kernelized Stein Discrepancy for Model-Based Reinforcement Learning

no code implementations2 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.

Continuous Control Model-based Reinforcement Learning +2

On the Hidden Biases of Policy Mirror Ascent in Continuous Action Spaces

no code implementations28 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.

Reinforcement Learning under a Multi-agent Predictive State Representation Model: Method and Theory

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.

reinforcement-learning Reinforcement Learning (RL)

Graph Neural Networks for Decentralized Multi-Robot Submodular Action Selection

1 code implementation18 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.

Decision Making Motion Planning

Multi-robot Symmetric Rendezvous Search on the Line

no code implementations13 Jan 2021 Deniz Ozsoyeller, Pratap Tokekar

We study the Symmetric Rendezvous Search Problem for a multi-robot system.

Robotics Discrete Mathematics

Multi-Agent Reinforcement Learning for Visibility-based Persistent Monitoring

1 code implementation2 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.

Graph Attention Multi-agent Reinforcement Learning +2

Coverage of an Environment Using Energy-Constrained Unmanned Aerial Vehicles

1 code implementation7 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.


Risk-Aware Planning and Assignment for Ground Vehicles using Uncertain Perception from Aerial Vehicles

no code implementations25 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.


Evaluation of Cross-View Matching to Improve Ground Vehicle Localization with Aerial Perception

no code implementations13 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.

Crop Height and Plot Estimation for Phenotyping from Unmanned Aerial Vehicles using 3D LiDAR

1 code implementation30 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.

View Planning and Navigation Algorithms for Autonomous Bridge Inspection with UAVs

1 code implementation3 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.


Distributed Attack-Robust Submodular Maximization for Multi-Robot Planning

no code implementations2 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.

Motion Planning

Risk-Aware Planning by Confidence Estimation using Deep Learning-Based Perception

no code implementations13 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.

Image Segmentation Semantic Segmentation

An Approximation Algorithm for Risk-averse Submodular Optimization

no code implementations24 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.

Combinatorial Optimization

Algorithms for Routing of Unmanned Aerial Vehicles with Mobile Recharging Stations

1 code implementation31 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

Radiation Search Operations using Scene Understanding with Autonomous UAV and UGV

no code implementations31 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.

Scene Segmentation Scene Understanding

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