Search Results for author:
Found 28 papers, 6 papers with code
Adaptive Visual Imitation Learning for Robotic Assisted Feeding Across Varied Bowl Configurations and Food Types
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.
Beyond Joint Demonstrations: Personalized Expert Guidance for Efficient Multi-Agent Reinforcement Learning
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.
Towards Efficient Risk-Sensitive Policy Gradient: An Iteration Complexity Analysis
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.
REBEL: A Regularization-Based Solution for Reward Overoptimization in Robotic Reinforcement Learning from Human Feedback
Current methods to mitigate this misalignment work by learning reward functions from human preferences; however, they inadvertently introduce a risk of reward overoptimization.
Enhancing Multi-Agent Coordination through Common Operating Picture Integration
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.
Pre-Trained Masked Image Model for Mobile Robot Navigation
2D top-down maps are commonly used for the navigation and exploration of mobile robots through unknown areas.
RE-MOVE: An Adaptive Policy Design for Robotic Navigation Tasks in Dynamic Environments via Language-Based Feedback
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.
Decision-Oriented Learning with Differentiable Submodular Maximization for Vehicle Routing Problem
We study the problem of learning a function that maps context observations (input) to parameters of a submodular function (output).
Where Am I Now? Dynamically Finding Optimal Sensor States to Minimize Localization Uncertainty for a Perception-Denied Rover
To find an optimal sensor state that minimizes the rover's localization uncertainty, DyFOS uses a localization uncertainty prediction pipeline in an optimization search.
Interpretable Deep Reinforcement Learning for Green Security Games with Real-Time Information
Green Security Games with real-time information (GSG-I) add the real-time information about the agents' movement to the typical GSG formulation.
Dealing with Sparse Rewards in Continuous Control Robotics via Heavy-Tailed Policies
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.
Posterior Coreset Construction with Kernelized Stein Discrepancy for Model-Based Reinforcement Learning
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.
On the Hidden Biases of Policy Mirror Ascent in Continuous Action Spaces
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
This paper proposes a new algorithm for learning the optimal policies under a novel multi-agent predictive state representation reinforcement learning model.
Graph Neural Networks for Decentralized Multi-Robot Submodular Action Selection
We demonstrate the performance of our GNN-based learning approach in a scenario of active target tracking with large networks of robots.
Multi-robot Symmetric Rendezvous Search on the Line
We study the Symmetric Rendezvous Search Problem for a multi-robot system.
Robotics Discrete Mathematics
Multi-Agent Reinforcement Learning for Visibility-based Persistent Monitoring
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.
Coverage of an Environment Using Energy-Constrained Unmanned Aerial Vehicles
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
Combining Geometric and Information-Theoretic Approaches for Multi-Robot Exploration
We present an algorithm to explore an orthogonal polygon using a team of $p$ robots.
Risk-Aware Planning and Assignment for Ground Vehicles using Uncertain Perception from Aerial Vehicles
Deep learning techniques can be used for semantic segmentation of the aerial image to create a cost map for safe ground robot navigation.
Robotics
Evaluation of Cross-View Matching to Improve Ground Vehicle Localization with Aerial Perception
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
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
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
Distributed Attack-Robust Submodular Maximization for Multi-Robot Planning
Since, DRM overestimates the number of attacks in each clique, in this paper we also introduce an Improved Distributed Robust Maximization (IDRM) algorithm.
Risk-Aware Planning by Confidence Estimation using Deep Learning-Based Perception
Images taken from the aerial view are used to provide a less obstructed map to guide the navigation of the robot on the ground.
An Approximation Algorithm for Risk-averse Submodular Optimization
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.
Algorithms for Routing of Unmanned Aerial Vehicles with Mobile Recharging Stations
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
Autonomously searching for hazardous radiation sources requires the ability of the aerial and ground systems to understand the scene they are scouting.
