24 accepted papers
As artificial intelligence (AI) becomes increasingly prevalent in society, there is a critical need for accessible K-12 educational resources that introduce students to AI and robotics concepts through engaging, hands-on experiences. In this paper, we present a scalable workshop framework that uses
Generative modeling of neural network parameters is often tied to architectures because standard parameter representations rely on known weight-matrix dimensions. Generation is further complicated by permutation symmetries that allow networks to model similar input-output functions while having wide…
Unmanned aerial vehicles (UAVs) require accurate odometry—i.e., estimating the position and velocity of the vehicle over time—as well as high-resolution sensing to safely and effectively operate in complex environments. Traditionally, GPS, cameras, and/or lidar sensors have been used to perform thes…
Current lidar and camera-based solutions for low-cost indoor mobile robots have limitations such as poor performance in visually obscured environments, high computational overhead for data processing, and high costs for lidars. In contrast, mmWave radar sensors offer a cost-effective and lightweight
Reinforcement learning (RL), while being the benchmark for policy formulation, often struggles to deliver robust solutions across varying scenarios, leading to marked performance drops under environmental perturbations.~Traditional adversarial training, based on a two-player max-min game, is known t…
In human-centric applications like healthcare and education, the \textit{heterogeneity} among patients and students necessitates personalized treatments and instructional interventions. While reinforcement learning (RL) has been utilized in those tasks, off-policy selection (OPS) is pivotal to close…
In the realm of reinforcement learning (RL), off-policy evaluation (OPE) holds a pivotal position, especially in high-stake human-involved scenarios such as e-learning and healthcare. Applying OPE to these domains is often challenging with scarce and underrepresentative offline training trajectories…
In this work, we introduce REFORMA, a novel robust reinforcement learning (RL) approach to design controllers for unmanned aerial vehicles (UAVs) robust to unknown disturbances during flights. These disturbances, typically due to wind turbulence, electromagnetic interference, temperature extremes an…
In this work, we present RadCloud, a novel real-time framework for directly obtaining higher-resolution lidar-like 2D point clouds from low-resolution radar frames on resource-constrained platforms commonly used in unmanned aerial and ground vehicles (UAVs and UGVs, respectively); such point clouds…
We present the first study on provably efficient randomized exploration in cooperative multi-agent reinforcement learning (MARL). We propose a unified algorithm framework for randomized exploration in parallel Markov Decision Processes (MDPs), and two Thompson Sampling (TS)-type algorithms, CoopTS-P…
Decision Transformers (DTs) have been highly effective for offline reinforcement learning (RL) tasks, successfully modeling the sequences of actions in a given set of demonstrations. However, DTs may perform poorly in stochastic environments, which are prevalent in robotics scenarios. In this paper,…
Visual servoing represents a control strategy capable of driving dynamical systems from the current to the desired pose, when the only available information is the images generated at both poses. In this work, we analyze vulnerability of such systems and introduce two types of attacks to deceive vis…
Off-policy evaluation (OPE) is important for closing the gap between offline training and evaluation of reinforcement learning (RL), by estimating performance and/or rank of target (evaluation) policies using offline trajectories only. It can improve the safety and efficiency of data collection and…
In this work, we study vulnerability of unmanned aerial vehicles (UAVs) to stealthy attacks on perception-based control. To guide our analysis, we consider two specific missions: (ii) ground vehicle tracking (GVT), and (ii) vertical take-off and landing (VTOL) of a quadcopter on a moving ground vehi…
Model-based methods have recently shown great potential for off-policy evaluation (OPE); offline trajectories induced by behavioral policies are fitted to transitions of Markov decision processes (MDPs), which are used to rollout simulated trajectories and estimate the performance of policies. Model…
Multivariate time series (MTS) classification is a challenging and important task in various domains and real-world applications. Much of prior work on MTS can be roughly divided into neural network (NN)- and pattern-based methods. The former can lead to robust classification performance, but many o…
In this work, we address the problem of formal safety verification for stochastic cyber-physical systems (CPS) equipped with ReLU neural network (NN) controllers. Our goal is to find the set of initial states from where, with a predetermined confidence, the system will not reach an unsafe configurat…
Though recent works have developed methods that can generate estimates (or imputations) of the missing entries in a dataset to facilitate downstream analysis, most depend on assumptions that may not align with real-world applications and could suffer from poor performance in subsequent tasks such as…
We study synthesis of control strategies from linear temporal logic (LTL) objectives in unknown environments. We model this problem as a turn-based zero-sum stochastic game between the controller and the environment, where the transition probabilities and the model topology are fully unknown. The wi…
We consider the problem of security-aware planning in an unknown stochastic environment, in the presence of attacks on control signals (i.e., actuators) of the robot. We model the attacker as an agent who has the full knowledge of the controller as well as the employed intrusion-detection system and…
We present a reinforcement learning (RL) frame-work to synthesize a control policy from a given linear temporal logic (LTL) specification in an unknown stochastic environment that can be modeled as a Markov Decision Process (MDP). Specifically, we learn a policy that maximizes the probability of sat…
In this paper, we propose a Deep Imitative Q-learning (DIQL) method to synthesize control policies for mobile robots that need to satisfy Linear Temporal Logic (LTL) specifications using noisy semantic observations of their surroundings. The robot sensing error is modeled using probabilistic labels…
There is a growing interest on formal methods-based robotic planning for temporal logic objectives. In this work, we extend the scope of existing synthesis methods to hyper-temporal logics. We are motivated by the fact that important planning objectives, such as optimality, robustness, and privacy,…
In this work, we synthesize collaboration protocols for human-unmanned aerial vehicle (H-UAV) command and control systems, where the human operator aids in securing the UAV by intermittently performing geolocation tasks to confirm its reported location. We first present a stochastic game-based model…