59 citations · 221 across the 105 of their papers we have counts for
17 papers · 1 filter
REBEL: Reward Regularization-Based Approach for Robotic Reinforcement Learning from Human Feedback
Souradip Chakraborty, Anukriti Singh, Amisha Bhaskar +3
The effectiveness of reinforcement learning (RL) agents in continuous control robotics tasks is mainly dependent on the design of the underlying reward function, which is highly pr…
Enhancing Multi-Agent Coordination through Common Operating Picture Integration
Peihong Yu, Bhoram Lee, Aswin Raghavan +3
In multi-agent systems, agents possess only local observations of the environment. Communication between teammates becomes crucial for enhancing coordination. Past research has pri…
Energy-Aware Route Planning for a Battery-Constrained Robot with Multiple Charging Depots
Ahmad Bilal Asghar, Pratap Tokekar
This paper considers energy-aware route planning for a battery-constrained robot operating in environments with multiple recharging depots. The robot has a battery discharge time $…
AG-CVG: Coverage Planning with a Mobile Recharging UGV and an Energy-Constrained UAV
Nare Karapetyan, Ahmad Bilal Asghar, Amisha Bhaskar +3
In this paper, we present an approach for coverage path planning for a team of an energy-constrained Unmanned Aerial Vehicle (UAV) and an Unmanned Ground Vehicle (UGV). Both the UA…
D2M2N: Decentralized Differentiable Memory-Enabled Mapping and Navigation for Multiple Robots
Md Ishat-E-Rabban, Pratap Tokekar
Recently, a number of learning-based models have been proposed for multi-robot navigation. However, these models lack memory and only rely on the current observations of the robot…
Decision-Oriented Learning Using Differentiable Submodular Maximization for Multi-Robot Coordination
Guangyao Shi, Chak Lam Shek, Nare Karapetyan +1
We present a differentiable, decision-oriented learning framework for cost prediction in a class of multi-robot decision-making problems, in which the robots need to trade off the…
LANCAR: Leveraging Language for Context-Aware Robot Locomotion in Unstructured Environments
Chak Lam Shek, Xiyang Wu, Wesley A. Suttle +5
Navigating robots through unstructured terrains is challenging, primarily due to the dynamic environmental changes. While humans adeptly navigate such terrains by using context fro…
Efficiently Identifying Hotspots in a Spatially Varying Field with Multiple Robots
Varun Suryan, Pratap Tokekar
In this paper, we present algorithms to identify environmental hotspots using mobile sensors. We examine two approaches: one involving a single robot and another using multiple rob…
UIVNAV: Underwater Information-driven Vision-based Navigation via Imitation Learning
Xiaomin Lin, Nare Karapetyan, Kaustubh Joshi +5
Autonomous navigation in the underwater environment is challenging due to limited visibility, dynamic changes, and the lack of a cost-efficient accurate localization system. We int…
A Survey of Decision-Theoretic Approaches for Robotic Environmental Monitoring
Yoonchang Sung, Zhiang Chen, Jnaneshwar Das +1
Robotics has dramatically increased our ability to gather data about our environments, creating an opportunity for the robotics and algorithms communities to collaborate on novel s…
Where to Drop Sensors from Aerial Robots to Monitor a Surface-Level Phenomenon?
Chak Lam Shek, Guangyao Shi, Ahmad Bilal Asghar +1
We consider the problem of routing a team of energy-constrained Unmanned Aerial Vehicles (UAVs) to drop unmovable sensors for monitoring a task area in the presence of stochastic w…
MAP-NBV: Multi-agent Prediction-guided Next-Best-View Planning for Active 3D Object Reconstruction
Harnaik Dhami, Vishnu D. Sharma, Pratap Tokekar
Next-Best View (NBV) planning is a long-standing problem of determining where to obtain the next best view of an object from, by a robot that is viewing the object. There are a num…
ProxMaP: Proximal Occupancy Map Prediction for Efficient Indoor Robot Navigation
Vishnu Dutt Sharma, Jingxi Chen, Pratap Tokekar
In a typical path planning pipeline for a ground robot, we build a map (e.g., an occupancy grid) of the environment as the robot moves around. While navigating indoors, a ground ro…
Pred-NBV: Prediction-guided Next-Best-View for 3D Object Reconstruction
Harnaik Dhami, Vishnu D. Sharma, Pratap Tokekar
Prediction-based active perception has shown the potential to improve the navigation efficiency and safety of the robot by anticipating the uncertainty in the unknown environment.…
RE-MOVE: An Adaptive Policy Design for Robotic Navigation Tasks in Dynamic Environments via Language-Based Feedback
Souradip Chakraborty, Kasun Weerakoon, Prithvi Poddar +6
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 resul…
Data-Driven Distributionally Robust Optimal Control with State-Dependent Noise
Rui Liu, Guangyao Shi, Pratap Tokekar
Distributionally Robust Optimal Control (DROC) is a framework that enables robust control in a stochastic setting where the true disturbance distribution is unknown. Traditional DR…
Decision-Oriented Learning with Differentiable Submodular Maximization for Vehicle Routing Problem
Guangyao Shi, Pratap Tokekar
We study the problem of learning a function that maps context observations (input) to parameters of a submodular function (output). Our motivating case study is a specific type of…