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Found 24 papers, 10 papers with code
Equivariant Ensembles and Regularization for Reinforcement Learning in Map-based Path Planning
In reinforcement learning (RL), exploiting environmental symmetries can significantly enhance efficiency, robustness, and performance.
Strict Partitioning for Sporadic Rigid Gang Tasks
In this paper, we propose a new partitioned scheduling strategy for rigid gang tasks, named strict partitioning.
Learning to Recharge: UAV Coverage Path Planning through Deep Reinforcement Learning
Coverage path planning (CPP) is a critical problem in robotics, where the goal is to find an efficient path that covers every point in an area of interest.
Edge Generation Scheduling for DAG Tasks Using Deep Reinforcement Learning
Using this schedulability test, we propose a new DAG scheduling framework (edge generation scheduling -- EGS) that attempts to minimize the DAG width by iteratively generating edges while guaranteeing the deadline constraint.
Secure-by-Construction Synthesis for Control Systems
These controllers are synthesized based on a concept of so-called (augmented) control barrier functions, which we introduce and discuss in detail.
Model-aided Federated Reinforcement Learning for Multi-UAV Trajectory Planning in IoT Networks
Deploying teams of unmanned aerial vehicles (UAVs) to harvest data from distributed Internet of Things (IoT) devices requires efficient trajectory planning and coordination algorithms.
Physical Deep Reinforcement Learning: Safety and Unknown Unknowns
In this paper, we propose the Phy-DRL: a physics-model-regulated deep reinforcement learning framework for safety-critical autonomous systems.
Physical Deep Reinforcement Learning Towards Safety Guarantee
Deep reinforcement learning (DRL) has achieved tremendous success in many complex decision-making tasks of autonomous systems with high-dimensional state and/or action spaces.
Residual Policy Learning for Vehicle Control of Autonomous Racing Cars
We propose a residual vehicle controller for autonomous racing cars that learns to amend a classical controller for the path-following of racing lines.
Learning to Generate All Feasible Actions
Several machine learning (ML) applications are characterized by searching for an optimal solution to a complex task.
Synergistic Redundancy: Towards Verifiable Safety for Autonomous Vehicles
The synergistic safety layer uses only verifiable and logically analyzable software to fulfill its tasks.
Verifiable Obstacle Detection
Perception of obstacles remains a critical safety concern for autonomous vehicles.
6IMPOSE: Bridging the Reality Gap in 6D Pose Estimation for Robotic Grasping
To overcome this gap, we introduce 6IMPOSE, a novel framework for sim-to-real data generation and 6D pose estimation.
Sandboxing (AI-based) Unverified Controllers in Stochastic Games: An Abstraction-based Approach with Safe-visor Architecture
In this paper, we propose a construction scheme for a Safe-visor architecture for sandboxing unverified controllers, e. g., artificial intelligence-based (a. k. a.
Cloud-Edge Training Architecture for Sim-to-Real Deep Reinforcement Learning
To overcome the reality gap, our architecture exploits sim-to-real transfer strategies to continue the training of simulation-pretrained agents on a physical system.
Formal Synthesis of Controllers for Uncertain Linear Systems against $ω$-Regular Properties: A Set-based Approach
Then, we compute the maximal HCI set over the state set of the product system by leveraging a set-based approach.
Sandboxing Controllers for Stochastic Cyber-Physical Systems
However, current available solutions for sandboxing controllers are just applicable to deterministic (a. k. a.
Risk Ranked Recall: Collision Safety Metric for Object Detection Systems in Autonomous Vehicles
Commonly used metrics for evaluation of object detection systems (precision, recall, mAP) do not give complete information about their suitability of use in safety critical tasks, like obstacle detection for collision avoidance in Autonomous Vehicles (AV).
Automata-based Controller Synthesis for Stochastic Systems: A Game Framework via Approximate Probabilistic Relations
In this work, we propose an abstraction and refinement methodology for the controller synthesis of discrete-time stochastic systems to enforce complex logical properties expressed by deterministic finite automata (a. k. a.
Safe-visor Architecture for Sandboxing (AI-based) Unverified Controllers in Stochastic Cyber-Physical Systems
To cope with this difficulty, we propose in this work a Safe-visor architecture for sandboxing unverified controllers in CPSs operating in noisy environments (a. k. a.
Multi-UAV Path Planning for Wireless Data Harvesting with Deep Reinforcement Learning
Harvesting data from distributed Internet of Things (IoT) devices with multiple autonomous unmanned aerial vehicles (UAVs) is a challenging problem requiring flexible path planning methods.
UAV Path Planning using Global and Local Map Information with Deep Reinforcement Learning
Path planning methods for autonomous unmanned aerial vehicles (UAVs) are typically designed for one specific type of mission.
UAV Path Planning for Wireless Data Harvesting: A Deep Reinforcement Learning Approach
Autonomous deployment of unmanned aerial vehicles (UAVs) supporting next-generation communication networks requires efficient trajectory planning methods.
UAV Coverage Path Planning under Varying Power Constraints using Deep Reinforcement Learning
Coverage path planning (CPP) is the task of designing a trajectory that enables a mobile agent to travel over every point of an area of interest.
Robotics Systems and Control Systems and Control
