Showing papers by "Roberto Tron published in 2023"

HoLA Robots: Mitigating Plan-Deviation Attacks in Multi-Robot Systems with Co-Observations and Horizon-Limiting Announcements

Abstract: Emerging multi-robot systems rely on cooperation between humans and robots, with robots following automatically generated motion plans to service application-level tasks. Given the safety requirements associated with operating in proximity to humans and expensive infrastructure, it is important to understand and mitigate the security vulnerabilities of such systems caused by compromised robots who diverge from their assigned plans. We focus on centralized systems, where a *central entity* (CE) is responsible for determining and transmitting the motion plans to the robots, which report their location as they move following the plan. The CE checks that robots follow their assigned plans by comparing their expected location to the location they self-report. We show that this self-reporting monitoring mechanism is vulnerable to *plan-deviation attacks* where compromised robots don't follow their assigned plans while trying to conceal their movement by mis-reporting their location. We propose a two-pronged mitigation for plan-deviation attacks: (1) an attack detection technique leveraging both the robots' local sensing capabilities to report observations of other robots and *co-observation schedules* generated by the CE, and (2) a prevention technique where the CE issues *horizon-limiting announcements* to the robots, reducing their instantaneous knowledge of forward lookahead steps in the global motion plan. On a large-scale automated warehouse benchmark, we show that our solution enables attack prevention guarantees from a stealthy attacker that has compromised multiple robots.

Localization Verifiability Under Outlier Measurements With Linear Programming

Abstract: The problem of localizing a set of nodes from relative pairwise measurements appears in different fields, such as computer vision, sensor networks, and robotics. In practice, the measurements might be contaminated by noise and outliers that lead to erroneous localization. Previous work has empirically shown that robust algorithms can, in some situations, almost completely cancel the effect of outliers. However, there is a theoretical gap in answering the following question: Under what conditions on the number, magnitude, and arrangement of the outlier measurements can we guarantee that a robust algorithm will recover the ground truth locations from the relative measurements alone? We denote this concept as verifiability, and answer the question for the case of an $\ell _{1}$-norm robust optimization formulation, with translation measurements that are affected only by large-magnitude outliers and no small-magnitude noise. We prove that verifiability depends only on the topology of the graph, the location of the edges affected by the outliers, and the sign of the outliers, while it is independent of the (a priori unknown) true location of the nodes, and the magnitude of the outliers. We present an algorithm based on the dual simplex algorithm that checks the verifiability of a problem, and, if not verifiable, completely characterizes the space of equivalent solutions that are consistent with the given pairwise measurements. Our theory and algorithms can be used to compute the a priori probability of recovering a solution congruent or equivalent to the ground truth, without having access to the true locations.

Efficient LQR-CBF-RRT*: Safe and Optimal Motion Planning

Abstract: Control Barrier Functions (CBF) are a powerful tool for designing safety-critical controllers and motion planners. The safety requirements are encoded as a continuously differentiable function that maps from state variables to a real value, in which the sign of its output determines whether safety is violated. In practice, the CBFs can be used to enforce safety by imposing itself as a constraint in a Quadratic Program (QP) solved point-wise in time. However, this approach costs computational resources and could lead to infeasibility in solving the QP. In this paper, we propose a novel motion planning framework that combines sampling-based methods with Linear Quadratic Regulator (LQR) and CBFs. Our approach does not require solving the QPs for control synthesis and avoids explicit collision checking during samplings. Instead, it uses LQR to generate optimal controls and CBF to reject unsafe trajectories. To improve sampling efficiency, we employ the Cross-Entropy Method (CEM) for importance sampling (IS) to sample configurations that will enhance the path with higher probability and store computed optimal gain matrices in a hash table to avoid re-computation during rewiring procedure. We demonstrate the effectiveness of our method on nonlinear control affine systems in simulation.

Robust Filtering based on Complex Cell Networks from the Visual Cortex

Abstract: We propose a novel approach for filtering that is inspired by Complex Cell Networks (CCN) in the primary visual cortex of mammals; our aim is to emulate the robustness of the biological system, showing graceful degradation in face of gross deterioration of the input. Instead of relying on energy minimization as in frequency-based filter design, or on Bayes’ theorem as in statistical filtering, our formulation is founded on three principles that have been observed in real neural responses: 1) winner-take-all, where perceptual ambiguity is solved by focusing on the strongest signal; 2) persistence, where information is fused across time to lessen the impact of noise, outliers, and temporary cancellations in the input data; and 3) boundedness, where the responses in the filter are bounded to be non-negative and below a maximum value. In neuroscience, the typical goal is to find models that match and explain measurements from a biological system. In this paper, we take an engineering approach, where we encode the three properties above as mathematical constraints, and find filter parameters that guarantee convergence of the filter (for constant, bounded inputs), optimize bounds on the convergence rate, and improve sparsity of the filter kernel; overall, the filter is obtained from the solution to a Linear Program (LP). As a proof-of-concept, we integrated the proposed filter architecture with a neural network to estimate the vehicle speed solely based on camera images in extremely noisy environments.

Byzantine Resilience at Swarm Scale: A Decentralized Blocklist Protocol from Inter-robot Accusations

Abstract: The Weighted-Mean Subsequence Reduced (W-MSR) algorithm, the state-of-the-art method for Byzantine-resilient design of decentralized multi-robot systems, is based on discarding outliers received over Linear Consensus Protocol (LCP). Although W-MSR provides well-understood theoretical guarantees relating robust network connectivity to the convergence of the underlying consensus, the method comes with several limitations preventing its use at scale: (1) the number of Byzantine robots, F, to tolerate should be known a priori, (2) the requirement that each robot maintains 2F+1 neighbors is impractical for large F, (3) information propagation is hindered by the requirement that F+1 robots independently make local measurements of the consensus property in order for the swarm's decision to change, and (4) W-MSR is specific to LCP and does not generalize to applications not implemented over LCP. In this work, we propose a Decentralized Blocklist Protocol (DBP) based on inter-robot accusations. Accusations are made on the basis of locally-made observations of misbehavior, and once shared by cooperative robots across the network are used as input to a graph matching algorithm that computes a blocklist. DBP generalizes to applications not implemented via LCP, is adaptive to the number of Byzantine robots, and allows for fast information propagation through the multi-robot system while simultaneously reducing the required network connectivity relative to W-MSR. On LCP-type applications, DBP reduces the worst-case connectivity requirement of W-MSR from (2F+1)-connected to (F+1)-connected and the number of cooperative observers required to propagate new information from F+1 to just 1 observer. We demonstrate empirically that our approach to Byzantine resilience scales to hundreds of robots on cooperative target tracking, time synchronization, and localization case studies.

Robustness Measures and Monitors for Time Window Temporal Logic

Abstract: Temporal logics (TLs) have been widely used to formalize interpretable tasks for cyber-physical systems. Time Window Temporal Logic (TWTL) has been recently proposed as a specification language for dynamical systems. In particular, it can easily express robotic tasks, and it allows for efficient, automata-based verification and synthesis of control policies for such systems. In this paper, we define two quantitative semantics for this logic, and two corresponding monitoring algorithms, which allow for real-time quantification of satisfaction of formulas by trajectories of discrete-time systems. We demonstrate the new semantics and their runtime monitors on numerical examples.