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1. Working Memory Augmentation for Improved Learning in Neural Adaptive Control

   https://doi.org/10.1109/CDC40024.2019.9029549  

   Muthirayan, Deepan; Khargonekar, Pramod P. (December 2019, 2019 IEEE Conference on Decision and Control)

   In this paper, we propose a novel control architecture, inspired from neuroscience, for adaptive control of continuous time systems. The objective here is to design control architectures and algorithms that can learn and adapt quickly to changes that are even abrupt. The proposed architecture, in the setting of standard neural network (NN) based adaptive control, augments an external working memory to the NN. The learning system stores, in its external working memory, recently observed feature vectors from the hidden layer of the NN that are relevant and forgets the older irrelevant values. It retrieves relevant vectors from the working memory to modify the final control signal generated by the controller. The use of external working memory improves the context inducing the learning system to search in a particular direction. This directed learning allows the learning system to find a good approximation of the unknown function even after abrupt changes quickly. We consider two classes of controllers for illustration of our ideas (i) a model reference NN adaptive controller for linear systems with matched uncertainty (ii) backstepping NN controller for strict feedback systems. Through extensive simulations and specific metrics we show that memory augmentation improves learning significantly even when the system undergoes sudden changes. Importantly, we also provide evidence for the proposed mechanism by which this specific memory augmentation improves learning. 

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2. Rule-Based Safe Probabilistic Movement Primitive Control via Control Barrier Functions

   https://doi.org/10.1109/TASE.2022.3217468  

   Davoodi, Mohammadreza; Iqbal, Asif; Cloud, Joseph M.; Beksi, William J.; Gans, Nicholas R. (January 2022, IEEE Transactions on Automation Science and Engineering)

   In this paper, we develop a novel and safe control design approach that takes demonstrations provided by a human teacher to enable a robot to accomplish complex manipulation scenarios in dynamic environments. First, an overall task is divided into multiple simpler subtasks that are more appropriate for learning and control objectives. Then, by collecting human demonstrations, the subtasks that require robot movement are modeled by probabilistic movement primitives (ProMPs). We also study two strategies for modifying the ProMPs to avoid collisions with environmental obstacles. Finally, we introduce a rule-base control technique by utilizing a finite-state machine along with a unique means of control design for ProMPs. For the ProMP controller, we propose control barrier and Lyapunov functions to guide the system along a trajectory within the distribution defined by a ProMP while guaranteeing that the system state never leaves more than a desired distance from the distribution mean. This allows for better performance on nonlinear systems and offers solid stability and known bounds on the system state. A series of simulations and experimental studies demonstrate the efficacy of our approach and show that it can run in real time. Note to Practitioners —This paper is motivated by the need to create a teach-by-demonstration framework that captures the strengths of movement primitives and verifiable, safe control. We provide a framework that learns safe control laws from a probability distribution of robot trajectories through the use of advanced nonlinear control that incorporates safety constraints. Typically, such distributions are stochastic, making it difficult to offer any guarantees on safe operation. Our approach ensures that the distribution of allowed robot trajectories is within an envelope of safety and allows for robust operation of a robot. Furthermore, using our framework various probability distributions can be combined to represent complex scenarios in the environment. It will benefit practitioners by making it substantially easier to test and deploy accurate, efficient, and safe robots in complex real-world scenarios. The approach is currently limited to scenarios involving static obstacles, with dynamic obstacle avoidance an avenue of future effort. 

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3. Automating CUDA Synchronization via Program Transformation

   https://doi.org/10.1109/ASE.2019.00075  

   Wu, Mingyuan; Zhang, Lingming; Liu, Cong; Tan, Shin Hwei; Zhang, Yuqun (November 2019, IEEE/ACM International Conference on Automated Software Engineering)

   While CUDA has been the most popular parallel computing platform and programming model for general purpose GPU computing, CUDA synchronization undergoes significant challenges for GPU programmers due to its intricate parallel computing mechanism and coding practices. In this paper, we propose AuCS, the first general framework to automate synchronization for CUDA kernel functions. AuCS transforms the original LLVM-level CUDA program control flow graph in a semantic-preserving manner for exploring the possible barrier function locations. Accordingly, AuCS develops mechanisms to correctly place barrier functions for automating synchronization in multiple erroneous (challenging-to-be-detected) synchronization scenarios, including data race, barrier divergence, and redundant barrier functions. To evaluate the effectiveness and efficiency of AuCS, we conduct an extensive set of experiments and the results demonstrate that AuCS can automate 20 out of 24 erroneous synchronization scenarios. 

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4. Protect the System Call, Protect (Most of) the World with BASTION

   https://doi.org/10.1145/3582016.3582066  

   Jelesnianski, Christopher; Ismail, Mohannad; Jang, Yeongjin; Williams, Dan; Min, Changwoo (March 2023, Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems)

   Tor M. Aamodt; Natalie D. Enright Jerger; Michael M. Swift (Ed.)

   System calls are a critical building block in many serious security attacks, such as control-flow hijacking and privilege escalation attacks. Security-sensitive system calls (e.g., execve, mprotect), especially play a major role in completing attacks. Yet, few defense efforts focus to ensure their legitimate usage, allowing attackers to maliciously leverage system calls in attacks. In this paper, we propose a novel System Call Integrity, which enforces the correct use of system calls throughout runtime. We propose three new contexts enforcing (1) which system call is called and how it is invoked (Call Type), (2) how a system call is reached (Control Flow), and (3) that arguments are not corrupted (Argument Integrity). Our defense mechanism thwarts attacks by breaking the critical building block in their attack chains. We implement Bastion, as a compiler and runtime monitor system, to demonstrate the efficacy of the three system call contexts. Our security case study shows that Bastion can effectively stop all the attacks including real-world exploits and recent advanced attack strategies. Deploying Bastion on three popular system call-intensive programs, NGINX, SQLite, and vsFTPd, we show Bastion is secure and practical, demonstrating overhead of 0.60%, 2.01%, and 1.65%, respectively 

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5. Lie Algebraic Cost Function Design for Control on Lie Groups

   Sangli Teng, William Clark (January 2022, Proc. IEEE CDC 2022)

   This paper presents a control framework on Lie groups by designing the control objective in its Lie algebra. Control on Lie groups is challenging due to its nonlinear nature and difficulties in system parameterization. Existing methods to design the control objective on a Lie group and then derive the gradient for controller design are non-trivial and can result in slow convergence in tracking control. We show that with a proper left-invariant metric, setting the gradient of the cost function as the tracking error in the Lie algebra leads to a quadratic Lyapunov function that enables globally exponential convergence. In the PD control case, we show that our controller can maintain an exponential convergence rate even when the initial error is approaching π in SO(3). We also show the merit of this proposed framework in trajectory optimization. The proposed cost function enables the iterative Linear Quadratic Regulator (iLQR) to converge much faster than the Differential Dynamic Programming (DDP) with a well-adopted cost function when the initial trajectory is poorly initialized on SO(3). 

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