Abstract--Increasingly, autonomous vehicles (AVs) are becoming a reality, such as the Advanced Driver Assistance Systems (ADAS) in vehicles that assist drivers in driving and parking functions with vehicles today. The localization problem for AVs relies primarily on multiple sensors, including cameras, LiDARs, and radars. Manufacturing, installing, calibrating, and maintaining these sensors can be very expensive, thereby increasing the overall cost of AVs. This research explores the means to improve localization on vehicles belonging to the ADAS category in a platooning context, where an ADAS vehicle follows a lead'Smart' AV equipped with a highly accurate sensor suite. We propose and produce results by using a filtering framework to combine pose information derived from vision and odometry to improve the localization of the ADAS vehicle that follows the smart vehicle.

Autonomous mobile robots (AMRs) equipped with high-quality cameras have revolutionized the field of inspections by providing efficient and cost-effective means of conducting surveys. The use of autonomous inspection is becoming more widespread in a variety of contexts, yet it is still challenging to acquire the best inspection information autonomously. In situations where objects may block a robot's view, it is necessary to use reasoning to determine the optimal points for collecting data. Although researchers have explored cloud-based applications to store inspection data, these applications may not operate optimally under network constraints, and parsing these datasets can be manually intensive. Instead, there is an emerging requirement for AMRs to autonomously capture the most informative views efficiently. To address this challenge, we present an autonomous Next-Best-View (NBV) framework that maximizes the inspection information while reducing the number of pictures needed during operations. The framework consists of a formalized evaluation metric using ray-tracing and Gaussian process interpolation to estimate information reward based on the current understanding of the partially-known environment. A derivative-free optimization (DFO) method is used to sample candidate views in the environment and identify the NBV point. The proposed approach's effectiveness is shown by comparing it with existing methods and further validated through simulations and experiments with various vehicles.

We study Markov potential games under the infinite horizon average reward criterion. Most previous studies have been for discounted rewards. We prove that both algorithms based on independent policy gradient and independent natural policy gradient converge globally to a Nash equilibrium for the average reward criterion. To set the stage for gradient-based methods, we first establish that the average reward is a smooth function of policies and provide sensitivity bounds for the differential value functions, under certain conditions on ergodicity and the second largest eigenvalue of the underlying Markov decision process (MDP). We prove that three algorithms, policy gradient, proximal-Q, and natural policy gradient (NPG), converge to an $\epsilon$-Nash equilibrium with time complexity $O(\frac{1}{\epsilon^2})$, given a gradient/differential Q function oracle. When policy gradients have to be estimated, we propose an algorithm with $\tilde{O}(\frac{1}{\min_{s,a}\pi(a|s)\delta})$ sample complexity to achieve $\delta$ approximation error w.r.t~the $\ell_2$ norm. Equipped with the estimator, we derive the first sample complexity analysis for a policy gradient ascent algorithm, featuring a sample complexity of $\tilde{O}(1/\epsilon^5)$. Simulation studies are presented.

Urban Air Mobility (UAM) expands vehicles from the ground to the near-ground space, envisioned as a revolution for transportation systems. Comprehensive scene perception is the foundation for autonomous aerial driving. However, UAM encounters the intelligent perception challenge: high perception learning requirements conflict with the limited sensors and computing chips of flying cars. To overcome the challenge, federated learning (FL) and other collaborative learning have been proposed to enable resource-limited devices to conduct onboard deep learning (DL) collaboratively. But traditional collaborative learning like FL relies on a central integrator for DL model aggregation, which is difficult to deploy in dynamic environments. The fully decentralized learning schemes may be the intuitive solution while the convergence of distributed learning cannot be guaranteed. Accordingly, this paper explores reconfigurable intelligent surfaces (RIS) empowered distributed learning, taking account of topological attributes to facilitate the learning performance with convergence guarantee. We propose several FL topological criteria for optimizing the transmission delay and convergence rate by exploiting the Laplacian matrix eigenvalues of the communication network. Subsequently, we innovatively leverage the RIS link modification ability to remold the current network according to the proposed topological criteria. This paper rethinks the functions of RIS from the perspective of the network layer. Furthermore, a deep deterministic policy gradient-based RIS phase shift control algorithm is developed to construct or deconstruct the network links simultaneously to reshape the communication network. Simulation experiments are conducted over MobileNet-based multi-view learning to verify the efficiency of the distributed FL framework.

Distributed control increases system scalability, flexibility, and redundancy. Foundational to such decentralisation is consensus formation, by which decision-making and coordination are achieved. However, decentralised multi-agent systems are inherently vulnerable to disruption. To develop a resilient consensus approach, inspiration is taken from the study of social systems and their dynamics; specifically, the Deffuant Model. A dynamic algorithm is presented enabling efficient consensus to be reached with an unknown number of disruptors present within a multi-agent system. By inverting typical social tolerance, agents filter out extremist non-standard opinions that would drive them away from consensus. This approach allows distributed systems to deal with unknown disruptions, without knowledge of the network topology or the numbers and behaviours of the disruptors. A disruptor-agnostic algorithm is particularly suitable to real-world applications where this information is typically unknown. Faster and tighter convergence can be achieved across a range of scenarios with the social dynamics inspired algorithm, compared with standard Mean-Subsequence-Reduced-type methods.

This paper studies Reinforcement Learning (RL) techniques to enable team coordination behaviors in graph environments with support actions among teammates to reduce the costs of traversing certain risky edges in a centralized manner. While classical approaches can solve this non-standard multi-agent path planning problem by converting the original Environment Graph (EG) into a Joint State Graph (JSG) to implicitly incorporate the support actions, those methods do not scale well to large graphs and teams. To address this curse of dimensionality, we propose to use RL to enable agents to learn such graph traversal and teammate supporting behaviors in a data-driven manner. Specifically, through a new formulation of the team coordination on graphs with risky edges problem into Markov Decision Processes (MDPs) with a novel state and action space, we investigate how RL can solve it in two paradigms: First, we use RL for a team of agents to learn how to coordinate and reach the goal with minimal cost on a single EG. We show that RL efficiently solves problems with up to 20/4 or 25/3 nodes/agents, using a fraction of the time needed for JSG to solve such complex problems; Second, we learn a general RL policy for any $N$-node EGs to produce efficient supporting behaviors. We present extensive experiments and compare our RL approaches against their classical counterparts.

Our method includes a scene-level objective connecting motion and environments, and an instance-level objective to refine learned representations. Our evaluations show that these objectives are complementary and outperform recent contrastive and autoencoding methods as pre-training for joint motion prediction. Furthermore, JointMotion adapts to all common types of environment representations used for motion prediction (i.e., agent-centric, scene-centric, and pairwise relative), and enables effective transfer learning between the Waymo Open Motion and the Argoverse 2 Forecasting datasets. Notably, our method improves the joint final displacement error of Wayformer, Scene Transformer, and HPTR by 3%, 7%, and 11%, respectively.

Collision detection via visual fences can significantly enhance the safety of collaborative robotic arms. Existing work typically performs such detection based on pre-deployed stationary cameras outside the robotic arm's workspace. These stationary cameras can only provide a restricted detection range and constrain the mobility of the robotic system. To cope with this issue, we propose an active sense method enabling a wide range of collision risk evaluation in dynamic scenarios. First, an active vision mechanism is implemented by equipping cameras with additional degrees of rotation. Considering the uncertainty in the active sense, we design a state confidence envelope to uniformly characterize both known and potential dynamic obstacles. Subsequently, using the observation-based uncertainty evolution, collision risk is evaluated by the prediction of obstacle envelopes. On this basis, a Markov decision process was employed to search for an optimal observation sequence of the active sense system, which enlarges the field of observation and reduces uncertainties in the state estimation of surrounding obstacles. Simulation and real-world experiments consistently demonstrate a 168% increase in the observation time coverage of typical dynamic humanoid obstacles compared to the method using stationary cameras, which underscores our system's effectiveness in collision risk tracking and enhancing the safety of robotic arms.

In this paper, we study the cooperative Multi-Agent Reinforcement Learning (MARL) problems using Reward Machines (RMs) to specify the reward functions such that the prior knowledge of high-level events in a task can be leveraged to facilitate the learning efficiency. Unlike the existing work that RMs have been incorporated into MARL for task decomposition and policy learning in relatively simple domains or with an assumption of independencies among the agents, we present Multi-Agent Reinforcement Learning with a Hierarchy of RMs (MAHRM) that is capable of dealing with more complex scenarios when the events among agents can occur concurrently and the agents are highly interdependent. MAHRM exploits the relationship of high-level events to decompose a task into a hierarchy of simpler subtasks that are assigned to a small group of agents, so as to reduce the overall computational complexity. Experimental results in three cooperative MARL domains show that MAHRM outperforms other MARL methods using the same prior knowledge of high-level events.

Despite the frequent use of agent-based models (ABMs) for studying social phenomena, parameter estimation remains a challenge, often relying on costly simulation-based heuristics. This work uses variational inference to estimate the parameters of an opinion dynamics ABM, by transforming the estimation problem into an optimization task that can be solved directly. Our proposal relies on probabilistic generative ABMs (PGABMs): we start by synthesizing a probabilistic generative model from the ABM rules. Then, we transform the inference process into an optimization problem suitable for automatic differentiation. In particular, we use the Gumbel-Softmax reparameterization for categorical agent attributes and stochastic variational inference for parameter estimation. Furthermore, we explore the trade-offs of using variational distributions with different complexity: normal distributions and normalizing flows. We validate our method on a bounded confidence model with agent roles (leaders and followers). Our approach estimates both macroscopic (bounded confidence intervals and backfire thresholds) and microscopic ($200$ categorical, agent-level roles) more accurately than simulation-based and MCMC methods. Consequently, our technique enables experts to tune and validate their ABMs against real-world observations, thus providing insights into human behavior in social systems via data-driven analysis.