- Design, Operation and Optimization of Smart Cyber-Physical Systems
Shreyas Ramakrishna is a graduate student in the Department of Electrical Engineering at Vanderbilt University, and works as a research assistant at the Institute for Software Integrated Systems. He received his M.S. degree in Electrical Engineering from Technical University Kaiserslautern (Germany) in June 2015 and completed his undergraduate studies in Electrical engineering from Visvesvaraya Technological University, India in 2012.
“Shreyas Ramakrishna Publications
S. Ramakrishna, Z. RahimiNasab, G. Karsai, A. Easwaran, and A. Dubey, Efficient Out-of-Distribution Detection Using Latent Space of β-VAE
for Cyber-Physical Systems, ACM Trans. Cyber-Phys. Syst., 2021.
@article{ramakrishna2021tcps,
author = {Ramakrishna, Shreyas and RahimiNasab, Zahra and Karsai, Gabor and Easwaran, Arvind and Dubey, Abhishek},
tag = {ai4cps},
title = {Efficient Out-of-Distribution Detection Using Latent Space of {\beta}-VAE
for Cyber-Physical Systems},
journal = {ACM Trans. Cyber-Phys. Syst.},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
year = {2021},
preprint = {https://arxiv.org/abs/2108.11800},
eprinttype = {arXiv}
}
Deep Neural Networks are actively being used in the design of autonomous Cyber-Physical Systems (CPSs). The advantage of these models is their ability to handle high-dimensional state-space and learn compact surrogate representations of the operational state spaces. However, the problem is that the sampled observations used for training the model may never cover the entire state space of the physical environment, and as a result, the system will likely operate in conditions that do not belong to the training distribution. These conditions that do not belong to training distribution are referred to as Out-of-Distribution (OOD). Detecting OOD conditions at runtime is critical for the safety of CPS. In addition, it is also desirable to identify the context or the feature(s) that are the source of OOD to select an appropriate control action to mitigate the consequences that may arise because of the OOD condition. In this paper, we study this problem as a multi-labeled time series OOD detection problem over images, where the OOD is defined both sequentially across short time windows (change points) as well as across the training data distribution. A common approach to solving this problem is the use of multi-chained one-class classifiers. However, this approach is expensive for CPSs that have limited computational resources and require short inference times. Our contribution is an approach to design and train a single β-Variational Autoencoder detector with a partially disentangled latent space sensitive to variations in image features. We use the feature sensitive latent variables in the latent space to detect OOD images and identify the most likely feature(s) responsible for the OOD. We demonstrate our approach using an Autonomous Vehicle in the CARLA simulator and a real-world automotive dataset called nuImages.
C. Hartsell, S. Ramakrishna, A. Dubey, D. Stojcsics, N. Mahadevan, and G. Karsai, ReSonAte: A Runtime Risk Assessment Framework for Autonomous Systems, in 16th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2021, 2021.
@inproceedings{resonate2021,
author = {Hartsell, Charles and Ramakrishna, Shreyas and Dubey, Abhishek and Stojcsics, Daniel and Mahadevan, Nag and Karsai, Gabor},
title = {ReSonAte: A Runtime Risk Assessment Framework for Autonomous Systems},
booktitle = {16th {International} Symposium on Software Engineering for Adaptive and Self-Managing Systems, {SEAMS} 2021},
year = {2021},
tag = {ai4cps},
category = {selectiveconference},
project = {cps-middleware,cps-reliability}
}
Autonomous Cyber-Physical Systems (CPSs) are often required to handle uncertainties and self-manage the system operation in response to problems and increasing risk in the operating paradigm. This risk may arise due to distribution shifts, environmental context, or failure of software or hardware components. Traditional techniques for risk assessment focus on design-time techniques such as hazard analysis, risk reduction, and assurance cases among others. However, these static, design time techniques do not consider the dynamic contexts and failures the systems face at runtime. We hypothesize that this requires a dynamic assurance approach that computes the likelihood of unsafe conditions or system failures considering the safety requirements, assumptions made at design time, past failures in a given operating context, and the likelihood of system component failures. We introduce the ReSonAte dynamic risk estimation framework for autonomous systems. ReSonAte reasons over Bow-Tie Diagrams (BTDs), which capture information about hazard propagation paths and control strategies. Our innovation is the extension of the BTD formalism with attributes for modeling the conditional relationships with the state of the system and environment. We also describe a technique for estimating these conditional relationships and equations for estimating risk-based on the state of the system and environment. To help with this process, we provide a scenario modeling procedure that can use the prior distributions of the scenes and threat conditions to generate the data required for estimating the conditional relationships. To improve scalability and reduce the amount of data required, this process considers each control strategy in isolation and composes several single-variate distributions into one complete multi-variate distribution for the control strategy in question. Lastly, we describe the effectiveness of our approach using two separate autonomous system simulations: CARLA and an unmanned underwater vehicle.
S. Ramakrishna, C. Harstell, M. P. Burruss, G. Karsai, and A. Dubey, Dynamic-weighted simplex strategy for learning enabled cyber physical systems, Journal of Systems Architecture, vol. 111, p. 101760, 2020.
@article{ramakrishna2020dynamic,
title = {Dynamic-weighted simplex strategy for learning enabled cyber physical systems},
journal = {Journal of Systems Architecture},
volume = {111},
pages = {101760},
year = {2020},
tag = {a14cps},
issn = {1383-7621},
doi = {https://doi.org/10.1016/j.sysarc.2020.101760},
url = {https://www.sciencedirect.com/science/article/pii/S1383762120300540},
author = {Ramakrishna, Shreyas and Harstell, Charles and Burruss, Matthew P. and Karsai, Gabor and Dubey, Abhishek},
keywords = {Convolutional Neural Networks, Learning Enabled Components, Reinforcement Learning, Simplex Architecture}
}
Cyber Physical Systems (CPS) have increasingly started using Learning Enabled Components (LECs) for performing perception-based control tasks. The simple design approach, and their capability to continuously learn has led to their widespread use in different autonomous applications. Despite their simplicity and impressive capabilities, these components are difficult to assure, which makes their use challenging. The problem of assuring CPS with untrusted controllers has been achieved using the Simplex Architecture. This architecture integrates the system to be assured with a safe controller and provides a decision logic to switch between the decisions of these controllers. However, the key challenges in using the Simplex Architecture are: (1) designing an effective decision logic, and (2) sudden transitions between controller decisions lead to inconsistent system performance. To address these research challenges, we make three key contributions: (1) dynamic-weighted simplex strategy – we introduce “weighted simplex strategy” as the weighted ensemble extension of the classical Simplex Architecture. We then provide a reinforcement learning based mechanism to find dynamic ensemble weights, (2) middleware framework – we design a framework that allows the use of the dynamic-weighted simplex strategy, and provides a resource manager to monitor the computational resources, and (3) hardware testbed – we design a remote-controlled car testbed called DeepNNCar to test and demonstrate the aforementioned key concepts. Using the hardware, we show that the dynamic-weighted simplex strategy has 60% fewer out-of-track occurrences (soft constraint violations), while demonstrating higher optimized speed (performance) of 0.4 m/s during indoor driving than the original LEC driven system.
V. Sundar, S. Ramakrishna, Z. Rahiminasab, A. Easwaran, and A. Dubey, Out-of-Distribution Detection in Multi-Label Datasets using Latent Space of β-VAE, in 2020 IEEE Security and Privacy Workshops (SPW), Los Alamitos, CA, USA, 2020, pp. 250–255.
@inproceedings{sundar2020detecting,
author = {Sundar, V. and Ramakrishna, S. and Rahiminasab, Z. and Easwaran, A. and Dubey, A.},
booktitle = {2020 IEEE Security and Privacy Workshops (SPW)},
title = {Out-of-Distribution Detection in Multi-Label Datasets using Latent Space of {\beta}-VAE},
year = {2020},
volume = {},
issn = {},
pages = {250-255},
keywords = {training;support vector machines;object detection;security;task analysis;testing;meteorology},
doi = {10.1109/SPW50608.2020.00057},
url = {https://doi.ieeecomputersociety.org/10.1109/SPW50608.2020.00057},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
tag = {ai4cps},
month = may
}
S. Ramakrishna, C. Hartsell, A. Dubey, P. Pal, and G. Karsai, A Methodology for Automating Assurance Case Generation, in Thirteenth International Tools and Methods of Competitive Engineering Symposium (TMCE 2020), 2020.
@inproceedings{ramakrishna2020methodology,
author = {Ramakrishna, Shreyas and Hartsell, Charles and Dubey, Abhishek and Pal, Partha and Karsai, Gabor},
title = {A Methodology for Automating Assurance Case Generation},
booktitle = {Thirteenth International Tools and Methods of Competitive Engineering Symposium (TMCE 2020)},
year = {2020},
tag = {ai4cps},
archiveprefix = {arXiv},
eprint = {2003.05388},
preprint = {https://arxiv.org/abs/2003.05388},
primaryclass = {cs.RO}
}
Safety Case has become an integral component for safety-certification in various Cyber Physical System domains including automotive, aviation, medical devices, and military. The certification processes for these systems are stringent and require robust safety assurance arguments and substantial evidence backing. Despite the strict requirements, current practices still rely on manual methods that are brittle, do not have a systematic approach or thorough consideration of sound arguments. In addition, stringent certification requirements and ever-increasing system complexity make ad-hoc, manual assurance case generation (ACG) inefficient, time consuming, and expensive. To improve the current state of practice, we introduce a structured ACG tool which uses system design artifacts, accumulated evidence, and developer expertise to construct a safety case and evaluate it in an automated manner. We also illustrate the applicability of the ACG tool on a remote-control car testbed case study.
C. Hartsell, N. Mahadevan, S. Ramakrishna, A. Dubey, T. Bapty, and G. Karsai, A CPS toolchain for learning-based systems: demo abstract, in Proceedings of the 10th ACM/IEEE International Conference on Cyber-Physical Systems, ICCPS 2019, Montreal, QC, Canada, 2019, pp. 342–343.
@inproceedings{Hartsell2019a,
author = {Hartsell, Charles and Mahadevan, Nagabhushan and Ramakrishna, Shreyas and Dubey, Abhishek and Bapty, Theodore and Karsai, Gabor},
title = {A {CPS} toolchain for learning-based systems: demo abstract},
booktitle = {Proceedings of the 10th {ACM/IEEE} International Conference on Cyber-Physical Systems, {ICCPS} 2019, Montreal, QC, Canada},
year = {2019},
pages = {342--343},
bibsource = {dblp computer science bibliography, https://dblp.org},
biburl = {https://dblp.org/rec/bib/conf/iccps/HartsellMRDBK19},
category = {poster},
doi = {10.1145/3302509.3313332},
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project = {cps-autonomy},
timestamp = {Sun, 07 Apr 2019 16:25:36 +0200},
url = {https://doi.org/10.1145/3302509.3313332}
}
Cyber-Physical Systems (CPS) are expected to perform tasks with ever-increasing levels of autonomy, often in highly uncertain environments. Traditional design techniques based on domain knowledge and analytical models are often unable to cope with epistemic uncertainties present in these systems. This challenge, combined with recent advances in machine learning, has led to the emergence of Learning-Enabled Components (LECs) in CPS. However, very little tool support is available for design automation of these systems. In this demonstration, we introduce an integrated toolchain for the development of CPS with LECs with support for architectural modeling, data collection, system software deployment, and LEC training, evaluation, and verification. Additionally, the toolchain supports the modeling and analysis of safety cases - a critical part of the engineering process for mission and safety critical systems.
M. P. Burruss, S. Ramakrishna, G. Karsai, and A. Dubey, DeepNNCar: A Testbed for Deploying and Testing Middleware Frameworks for Autonomous Robots, in IEEE 22nd International Symposium on Real-Time Distributed Computing, ISORC 2019, Valencia, Spain, May 7-9, 2019, 2019, pp. 87–88.
@inproceedings{Burruss2019,
author = {Burruss, Matthew P. and Ramakrishna, Shreyas and Karsai, Gabor and Dubey, Abhishek},
title = {DeepNNCar: {A} Testbed for Deploying and Testing Middleware Frameworks for Autonomous Robots},
booktitle = {{IEEE} 22nd International Symposium on Real-Time Distributed Computing, {ISORC} 2019, Valencia, Spain, May 7-9, 2019},
year = {2019},
tag = {ai4cps},
pages = {87--88},
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timestamp = {Wed, 16 Oct 2019 14:14:53 +0200},
url = {https://doi.org/10.1109/ISORC.2019.00025}
}
This demo showcases the features of an adaptive middleware framework for resource constrained autonomous robots like DeepNNCar (Figure 1). These robots use Learning Enabled Components (LECs), trained with deep learning models to perform control actions. However, these LECs do not provide any safety guarantees and testing them is challenging. To overcome these challenges, we have developed an adaptive middleware framework that (1) augments the LEC with safety controllers that can use different weighted simplex strategies to improve the systems safety guarantees, and (2) includes a resource manager to monitor the resource parameters (temperature, CPU Utilization), and offload tasks at runtime. Using DeepNNCar we will demonstrate the framework and its capability to adaptively switch between the controllers and strategies based on its safety and speed performance.
S. Ramakrishna, A. Dubey, M. P. Burruss, C. Hartsell, N. Mahadevan, S. Nannapaneni, A. Laszka, and G. Karsai, Augmenting Learning Components for Safety in Resource Constrained Autonomous Robots, in IEEE 22nd International Symposium on Real-Time Distributed Computing, ISORC 2019, Valencia, Spain, May 7-9, 2019, 2019, pp. 108–117.
@inproceedings{Ramakrishna2019,
author = {Ramakrishna, Shreyas and Dubey, Abhishek and Burruss, Matthew P. and Hartsell, Charles and Mahadevan, Nagabhushan and Nannapaneni, Saideep and Laszka, Aron and Karsai, Gabor},
title = {Augmenting Learning Components for Safety in Resource Constrained Autonomous Robots},
booktitle = {{IEEE} 22nd International Symposium on Real-Time Distributed Computing, {ISORC} 2019, Valencia, Spain, May 7-9, 2019},
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}
Learning enabled components (LECs) trained using data-driven algorithms are increasingly being used in autonomous robots commonly found in factories, hospitals, and educational laboratories. However, these LECs do not provide any safety guarantees, and testing them is challenging. In this paper, we introduce a framework that performs weighted simplex strategy based supervised safety control, resource management and confidence estimation of autonomous robots. Specifically, we describe two weighted simplex strategies: (a) simple weighted simplex strategy (SW-Simplex) that computes a weighted controller output by comparing the decisions between a safety supervisor and an LEC, and (b) a context-sensitive weighted simplex strategy (CSW-Simplex) that computes a context-aware weighted controller output. We use reinforcement learning to learn the contextual weights. We also introduce a system monitor that uses the current state information and a Bayesian network model learned from past data to estimate the probability of the robotic system staying in the safe working region. To aid resource constrained robots in performing complex computations of these weighted simplex strategies, we describe a resource manager that offloads tasks to an available fog nodes. The paper also describes a hardware testbed called DeepNNCar, which is a low cost resource-constrained RC car, built to perform autonomous driving. Using the hardware, we show that both SW-Simplex and CSW-Simplex have 40% and 60% fewer safety violations, while demonstrating higher optimized speed during indoor driving around 0.40m/s than the original system (using only LECs).
C. Hartsell, N. Mahadevan, S. Ramakrishna, A. Dubey, T. Bapty, T. T. Johnson, X. D. Koutsoukos, J. Sztipanovits, and G. Karsai, Model-based design for CPS with learning-enabled components, in Proceedings of the Workshop on Design Automation for CPS and IoT, DESTION@CPSIoTWeek 2019, Montreal, QC, Canada, 2019, pp. 1–9.
@inproceedings{Hartsell2019,
author = {Hartsell, Charles and Mahadevan, Nagabhushan and Ramakrishna, Shreyas and Dubey, Abhishek and Bapty, Theodore and Johnson, Taylor T. and Koutsoukos, Xenofon D. and Sztipanovits, Janos and Karsai, Gabor},
title = {Model-based design for {CPS} with learning-enabled components},
booktitle = {Proceedings of the Workshop on Design Automation for {CPS} and IoT, DESTION@CPSIoTWeek 2019, Montreal, QC, Canada},
year = {2019},
tag = {ai4cps},
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}
Recent advances in machine learning led to the appearance of Learning-Enabled Components (LECs) in Cyber-Physical Systems. LECs are being evaluated and used for various, complex functions including perception and control. However, very little tool support is available for design automation in such systems. This paper introduces an integrated toolchain that supports the architectural modeling of CPS with LECs, but also has extensive support for the engineering and integration of LECs, including support for training data collection, LEC training, LEC evaluation and verification, and system software deployment. Additionally, the toolsuite supports the modeling and analysis of safety cases - a critical part of the engineering process for mission and safety critical systems.
C. Hartsell, N. Mahadevan, S. Ramakrishna, A. Dubey, T. Bapty, T. T. Johnson, X. D. Koutsoukos, J. Sztipanovits, and G. Karsai, CPS Design with Learning-Enabled Components: A Case Study, in Proceedings of the 30th International Workshop on Rapid System Prototyping, RSP 2019, New York, NY, USA, October 17-18, 2019, 2019, pp. 57–63.
@inproceedings{Hartsell2019b,
author = {Hartsell, Charles and Mahadevan, Nagabhushan and Ramakrishna, Shreyas and Dubey, Abhishek and Bapty, Theodore and Johnson, Taylor T. and Koutsoukos, Xenofon D. and Sztipanovits, Janos and Karsai, Gabor},
title = {{CPS} Design with Learning-Enabled Components: {A} Case Study},
booktitle = {Proceedings of the 30th International Workshop on Rapid System Prototyping, {RSP} 2019, New York, NY, USA, October 17-18, 2019},
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pages = {57--63},
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timestamp = {Thu, 28 Nov 2019 12:43:50 +0100},
url = {https://doi.org/10.1145/3339985.3358491}
}
Cyber-Physical Systems (CPS) are used in many applications where they must perform complex tasks with a high degree of autonomy in uncertain environments. Traditional design flows based on domain knowledge and analytical models are often impractical for tasks such as perception, planning in uncertain environments, control with ill-defined objectives, etc. Machine learning based techniques have demonstrated good performance for such difficult tasks, leading to the introduction of Learning-Enabled Components (LEC) in CPS. Model based design techniques have been successful in the development of traditional CPS, and toolchains which apply these techniques to CPS with LECs are being actively developed. As LECs are critically dependent on training and data, one of the key challenges is to build design automation for them. In this paper, we examine the development of an autonomous Unmanned Underwater Vehicle (UUV) using the Assurance-based Learning-enabled Cyber-physical systems (ALC) Toolchain. Each stage of the development cycle is described including architectural modeling, data collection, LEC training, LEC evaluation and verification, and system-level assurance.
