223 citations · 645 across the 31 of their papers we have counts for
32 papers
Co-Optimizing Cache Partitioning and Multi-Core Task Scheduling: Exploit Cache Sensitivity or Not?
Binqi Sun, Debayan Roy, Tomasz Kloda +3
Cache partitioning techniques have been successfully adopted to mitigate interference among concurrently executing real-time tasks on multi-core processors. Considering that the ex…
Edge Generation Scheduling for DAG Tasks Using Deep Reinforcement Learning
Binqi Sun, Mirco Theile, Ziyuan Qin +4
Directed acyclic graph (DAG) tasks are currently adopted in the real-time domain to model complex applications from the automotive, avionics, and industrial domains that implement…
Secure-by-Construction Synthesis for Control Systems
Bingzhuo Zhong, Siyuan Liu, Marco Caccamo +1
In this paper, we present the synthesis of secure-by-construction controllers that address safety and security properties simultaneously in cyber-physical systems. Our focus is on…
Model-aided Federated Reinforcement Learning for Multi-UAV Trajectory Planning in IoT Networks
Jichao Chen, Omid Esrafilian, Harald Bayerlein +2
Deploying teams of unmanned aerial vehicles (UAVs) to harvest data from distributed Internet of Things (IoT) devices requires efficient trajectory planning and coordination algorit…
Efficient Learning of Urban Driving Policies Using Bird's-Eye-View State Representations
Raphael Trumpp, Martin Büchner, Abhinav Valada +1
Autonomous driving involves complex decision-making in highly interactive environments, requiring thoughtful negotiation with other traffic participants. While reinforcement learni…
Physics-Regulated Deep Reinforcement Learning: Invariant Embeddings
Hongpeng Cao, Yanbing Mao, Lui Sha +1
This paper proposes the Phy-DRL: a physics-regulated deep reinforcement learning (DRL) framework for safety-critical autonomous systems. The Phy-DRL has three distinguished invaria…
Physical Deep Reinforcement Learning Towards Safety Guarantee
Hongpeng Cao, Yanbing Mao, Lui Sha +1
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. Howe…
Flexible Gear Assembly With Visual Servoing and Force Feedback
Junjie Ming, Daniel Bargmann, Hongpeng Cao +1
Gear assembly is an essential but challenging task in industrial automation. This paper presents a novel two-stage approach for achieving high-precision and flexible gear assembly.…
Residual Policy Learning for Vehicle Control of Autonomous Racing Cars
Raphael Trumpp, Denis Hoornaert, Marco Caccamo
The development of vehicle controllers for autonomous racing is challenging because racing cars operate at their physical driving limit. Prompted by the demand for improved perform…
Learning to Generate All Feasible Actions
Mirco Theile, Daniele Bernardini, Raphael Trumpp +3
Modern cyber-physical systems are becoming increasingly complex to model, thus motivating data-driven techniques such as reinforcement learning (RL) to find appropriate control age…
Perception Simplex: Verifiable Collision Avoidance in Autonomous Vehicles Amidst Obstacle Detection Faults
Ayoosh Bansal, Hunmin Kim, Simon Yu +4
Advances in deep learning have revolutionized cyber-physical applications, including the development of Autonomous Vehicles. However, real-world collisions involving autonomous con…
Verifiable Obstacle Detection
Ayoosh Bansal, Hunmin Kim, Simon Yu +4
Perception of obstacles remains a critical safety concern for autonomous vehicles. Real-world collisions have shown that the autonomy faults leading to fatal collisions originate f…
6IMPOSE: Bridging the Reality Gap in 6D Pose Estimation for Robotic Grasping
Hongpeng Cao, Lukas Dirnberger, Daniele Bernardini +2
6D pose recognition has been a crucial factor in the success of robotic grasping, and recent deep learning based approaches have achieved remarkable results on benchmarks. However,…
Synthesizing Safety Controllers for Uncertain Linear Systems: A Direct Data-driven Approach
Bingzhuo Zhong, Majid Zamani, Marco Caccamo
In this paper, we provide a direct data-driven approach to synthesize safety controllers for unknown linear systems affected by unknown-but-bounded disturbances, in which identifyi…
Sandboxing (AI-based) Unverified Controllers in Stochastic Games: An Abstraction-based Approach with Safe-visor Architecture
Bingzhuo Zhong, Hongpeng Cao, Majid Zamani +1
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. AI-based) controlle…
Cloud-Edge Training Architecture for Sim-to-Real Deep Reinforcement Learning
Hongpeng Cao, Mirco Theile, Federico G. Wyrwal +1
Deep reinforcement learning (DRL) is a promising approach to solve complex control tasks by learning policies through interactions with the environment. However, the training of DR…
Formal Synthesis of Controllers for Uncertain Linear Systems against -Regular Properties: A Set-based Approach
Bingzhuo Zhong, Majid Zamani, Marco Caccamo
In this paper, we present how to synthesize controllers to enforce -regular properties over linear control systems affected by bounded disturbances. In particular, these control…
Sandboxing Controllers for Stochastic Cyber-Physical Systems
Bingzhuo Zhong, Majid Zamani, Marco Caccamo
Current cyber-physical systems (CPS) are expected to accomplish complex tasks. To achieve this goal, high performance, but unverified controllers (e.g. deep neural network, black-b…
Multi-Agent Belief Sharing through Autonomous Hierarchical Multi-Level Clustering
Mirco Theile, Jonathan Ponniah, Or Dantsker +1
Coordination in multi-agent systems is challenging for agile robots such as unmanned aerial vehicles (UAVs), where relative agent positions frequently change due to unconstrained m…
Risk Ranked Recall: Collision Safety Metric for Object Detection Systems in Autonomous Vehicles
Ayoosh Bansal, Jayati Singh, Micaela Verucchi +2
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,…
SchedGuard: Protecting against Schedule Leaks Using Linux Containers
Jiyang Chen, Tomasz Kloda, Ayoosh Bansal +6
Real-time systems have recently been shown to be vulnerable to timing inference attacks, mainly due to their predictable behavioral patterns. Existing solutions such as schedule ra…
Automata-based Controller Synthesis for Stochastic Systems: A Game Framework via Approximate Probabilistic Relations
Bingzhuo Zhong, Abolfazl Lavaei, Majid Zamani +1
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…
Safe-visor Architecture for Sandboxing (AI-based) Unverified Controllers in Stochastic Cyber-Physical Systems
Bingzhuo Zhong, Abolfazl Lavaei, Hongpeng Cao +2
High performance but unverified controllers, e.g., artificial intelligence-based (a.k.a. AI-based) controllers, are widely employed in cyber-physical systems (CPSs) to accomplish c…
Multi-UAV Path Planning for Wireless Data Harvesting with Deep Reinforcement Learning
Harald Bayerlein, Mirco Theile, Marco Caccamo +1
Harvesting data from distributed Internet of Things (IoT) devices with multiple autonomous unmanned aerial vehicles (UAVs) is a challenging problem requiring flexible path planning…