no code implementations • 19 Mar 2024 • Mirco Theile, Hongpeng Cao, Marco Caccamo, Alberto L. Sangiovanni-Vincentelli
In reinforcement learning (RL), exploiting environmental symmetries can significantly enhance efficiency, robustness, and performance.
no code implementations • 15 Mar 2024 • Binqi Sun, Tomasz Kloda, Marco Caccamo
In this paper, we propose a new partitioned scheduling strategy for rigid gang tasks, named strict partitioning.
1 code implementation • 6 Sep 2023 • Mirco Theile, Harald Bayerlein, Marco Caccamo, Alberto L. Sangiovanni-Vincentelli
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.
1 code implementation • 28 Aug 2023 • Binqi Sun, Mirco Theile, Ziyuan Qin, Daniele Bernardini, Debayan Roy, Andrea Bastoni, Marco Caccamo
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.
no code implementations • 5 Jul 2023 • Bingzhuo Zhong, Siyuan Liu, Marco Caccamo, Majid Zamani
These controllers are synthesized based on a concept of so-called (augmented) control barrier functions, which we introduce and discuss in detail.
1 code implementation • 3 Jun 2023 • Jichao Chen, Omid Esrafilian, Harald Bayerlein, David Gesbert, Marco Caccamo
Deploying teams of unmanned aerial vehicles (UAVs) to harvest data from distributed Internet of Things (IoT) devices requires efficient trajectory planning and coordination algorithms.
no code implementations • 26 May 2023 • Hongpeng Cao, Yanbing Mao, Lui Sha, Marco Caccamo
In this paper, we propose the Phy-DRL: a physics-model-regulated deep reinforcement learning framework for safety-critical autonomous systems.
no code implementations • 29 Mar 2023 • Hongpeng Cao, Yanbing Mao, Lui Sha, Marco Caccamo
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.
1 code implementation • 14 Feb 2023 • Raphael Trumpp, Denis Hoornaert, Marco Caccamo
We propose a residual vehicle controller for autonomous racing cars that learns to amend a classical controller for the path-following of racing lines.
no code implementations • 26 Jan 2023 • Mirco Theile, Daniele Bernardini, Raphael Trumpp, Cristina Piazza, Marco Caccamo, Alberto L. Sangiovanni-Vincentelli
Several machine learning (ML) applications are characterized by searching for an optimal solution to a complex task.
no code implementations • 4 Sep 2022 • Ayoosh Bansal, Simon Yu, Hunmin Kim, Bo Li, Naira Hovakimyan, Marco Caccamo, Lui Sha
The synergistic safety layer uses only verifiable and logically analyzable software to fulfill its tasks.
1 code implementation • 30 Aug 2022 • Hongpeng Cao, Lukas Dirnberger, Daniele Bernardini, Cristina Piazza, Marco Caccamo
To overcome this gap, we introduce 6IMPOSE, a novel framework for sim-to-real data generation and 6D pose estimation.
1 code implementation • 30 Aug 2022 • Ayoosh Bansal, Hunmin Kim, Simon Yu, Bo Li, Naira Hovakimyan, Marco Caccamo, Lui Sha
Perception of obstacles remains a critical safety concern for autonomous vehicles.
no code implementations • 28 Mar 2022 • Bingzhuo Zhong, Hongpeng Cao, Majid Zamani, Marco Caccamo
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.
no code implementations • 4 Mar 2022 • Hongpeng Cao, Mirco Theile, Federico G. Wyrwal, Marco Caccamo
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.
no code implementations • 16 Nov 2021 • Bingzhuo Zhong, Majid Zamani, Marco Caccamo
Then, we compute the maximal HCI set over the state set of the product system by leveraging a set-based approach.
no code implementations • 23 Sep 2021 • Bingzhuo Zhong, Majid Zamani, Marco Caccamo
However, current available solutions for sandboxing controllers are just applicable to deterministic (a. k. a.
no code implementations • 8 Jun 2021 • Ayoosh Bansal, Jayati Singh, Micaela Verucchi, Marco Caccamo, Lui Sha
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).
no code implementations • 23 Apr 2021 • Bingzhuo Zhong, Abolfazl Lavaei, Majid Zamani, Marco Caccamo
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.
no code implementations • 10 Feb 2021 • Bingzhuo Zhong, Abolfazl Lavaei, Hongpeng Cao, Majid Zamani, Marco Caccamo
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.
1 code implementation • 23 Oct 2020 • Harald Bayerlein, Mirco Theile, Marco Caccamo, David Gesbert
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.
2 code implementations • 14 Oct 2020 • Mirco Theile, Harald Bayerlein, Richard Nai, David Gesbert, Marco Caccamo
Path planning methods for autonomous unmanned aerial vehicles (UAVs) are typically designed for one specific type of mission.
3 code implementations • 1 Jul 2020 • Harald Bayerlein, Mirco Theile, Marco Caccamo, David Gesbert
Autonomous deployment of unmanned aerial vehicles (UAVs) supporting next-generation communication networks requires efficient trajectory planning methods.
2 code implementations • 5 Mar 2020 • Mirco Theile, Harald Bayerlein, Richard Nai, David Gesbert, Marco Caccamo
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