Secure State Estimation: Optimal Guarantees against Sensor Attacks in the Presence of Noise

Motivated by the need to secure cyber-physical systems against attacks, we consider the problem of estimating the state of a noisy linear dynamical system when a subset of sensors is arbitrarily corrupted by an adversary. We propose a secure state estimation algorithm and derive (optimal) bounds on the achievable state estimation error. In addition, as a result of independent interest, we give a coding theoretic interpretation for prior work on secure state estimation against sensor attacks in a noiseless dynamical system.Comment: A shorter version of this work will appear in the proceedings of ISIT 201

Application-based COVID-19 micro-mobility solution for safe and smart navigation in pandemics

Short distance travel and commute being inevitable, safe route planning in pandemics for micro-mobility, i.e., cycling and walking, is extremely important for the safety of oneself and others. Hence, we propose an application-based solution using COVID-19 occurrence data and a multi-criteria route planning technique for cyclists and pedestrians. This study aims at objectively determining the routes based on various criteria on COVID-19 safety of a given route while keeping the user away from potential COVID-19 transmission spots. The vulnerable spots include places such as a hospital or medical zones, contained residential areas, and roads with a high connectivity and influx of people. The proposed algorithm returns a multi-criteria route modeled on COVID-19-modified parameters of micro-mobility and betweenness centrality considering COVID-19 avoidance as well as the shortest available safe route for user ease and shortened time of outside environment exposure. We verified our routing algorithm in a part of Delhi, India, by visualizing containment zones and medical establishments. The results with COVID-19 data analysis and route planning suggest a safer route in the context of the coronavirus outbreak as compared to normal navigation and on average route extension is within 8%–12%. Moreover, for further advancement and post-COVID-19 era, we discuss the need for adding open data policy and the spatial system architecture for data usage, as a part of a pandemic strategy. The study contributes new micro-mobility parameters adapted for COVID-19 and policy guidelines based on aggregated contact tracing data analysis maintaining privacy, security, and anonymity

Object-Centric Perception for Real-World Robotics

Deep learning has resulted in incredible progress in many applications of artificial intelligence.However, these techniques often fall short when applied to robotics, due to their inability to reason about the ambiguity that often arises in the real world. Much of this ambiguity stems from the real world’s long-tail visual diversity – in particular, the huge variety of objects that robots must interact with. Such shortcomings are only exacerbated by the strict requirements for autonomous, high-throughput operation that deployed systems must meet, as well as the cost and difficulty of obtaining the large-scale training datasets that modern deep learning methods require.In this thesis, we explore two primary avenues of addressing these challenges. First, we introduce models that can better express uncertainty in challenging or ambiguous situations, across a variety of 2D and 3D perception tasks. Real-world robots can incorporate these models to reason explicitly about ambiguity, in flexible ways depending on their specific tasks. Second, we extend the capabilities of neural renderers to develop a sim2real2sim method that can drastically reduce the amount of data needed to train such models. From only a handful of in-the-wild examples, our method learns to generate synthetic scenes, targeted to specific real objects and environments, that can be used to train downstream perception models for a variety of tasks