In this paper, we view a policy or plan as a transition system over a space of information states that reflect a robot's or other observer's perspective based on limited sensing, memory, computation, and actuation. Regardless of whether policies are obtained by learning algorithms, planning algorithms, or human insight, we want to know the limits of feasibility for given robot hardware and tasks. Toward the quest to find the best policies, we establish in a general setting that minimal information transition systems (ITSs) exist up to reasonable equivalence assumptions, and are unique under some general conditions. We then apply the theory to generate new insights into several problems, including optimal sensor fusion/filtering, solving basic planning tasks, and finding minimal representations for feasible policies.

Action abstractions restrict the number of legal actions available for real-time planning in zero-sum extensive-form games, thus allowing algorithms to focus their search on a set of promising actions. Even though unabstracted game trees can lead to optimal policies, due to real-time constraints and the tree size, they are not a practical choice. In this context, we introduce an action abstraction scheme which we call asymmetric action abstraction. Asymmetric abstractions allow search algorithms to "pay more attention" to some aspects of the game by unevenly dividing the algorithm's search effort amongst different aspects of the game. We also introduce four algorithms that search in asymmetrically abstracted game trees to evaluate the effectiveness of our abstraction schemes. Two of our algorithms are adaptations of algorithms developed for searching in action-abstracted spaces, Portfolio Greedy Search and Stratified Strategy Selection, and the other two are adaptations of an algorithm developed for searching in unabstracted spaces, NaïveMCTS. An extensive set of experiments in a real-time strategy game shows that search algorithms using asymmetric abstractions are able to outperform all other search algorithms tested.

With the rapid development of cloud computing, virtual machine scheduling has become one of the most important but challenging issues for the cloud computing community, especially for practical heterogeneous request sequences. By analyzing the impact of request heterogeneity on some popular heuristic schedulers, it can be found that existing scheduling algorithms can not handle the request heterogeneity properly and efficiently. In this paper, a plug-and-play virtual machine scheduling intensifier, called Resource Assigner (ReAssigner), is proposed to enhance the scheduling efficiency of any given scheduler for heterogeneous requests. The key idea of ReAssigner is to pre-assign roles to physical resources and let resources of the same role form a virtual cluster to handle homogeneous requests. ReAssigner can cooperate with arbitrary schedulers by restricting their scheduling space to virtual clusters. With evaluations on the real dataset from Huawei Cloud, the proposed ReAssigner achieves significant scheduling performance improvement compared with some state-of-the-art scheduling methods.

Object-goal navigation (Object-nav) entails searching, recognizing and navigating to a target object. Object-nav has been extensively studied by the Embodied-AI community, but most solutions are often restricted to considering static objects (e.g., television, fridge, etc.). We propose a modular framework for object-nav that is able to efficiently search indoor environments for not just static objects but also movable objects (e.g. fruits, glasses, phones, etc.) that frequently change their positions due to human intervention. Our contextual-bandit agent efficiently explores the environment by showing optimism in the face of uncertainty and learns a model of the likelihood of spotting different objects from each navigable location. The likelihoods are used as rewards in a weighted minimum latency solver to deduce a trajectory for the robot. We evaluate our algorithms in two simulated environments and a real-world setting, to demonstrate high sample efficiency and reliability.

Utilizing autonomous drones or unmanned aerial vehicles (UAVs) has shown great advantages over preceding methods in support of urgent scenarios such as search and rescue (SAR) and wildfire detection. In these operations, search efficiency in terms of the amount of time spent to find the target is crucial since with the passing of time the survivability of the missing person decreases or wildfire management becomes more difficult with disastrous consequences. In this work, it is considered a scenario where a drone is intended to search and detect a missing person (e.g., a hiker or a mountaineer) or a potential fire spot in a given area. In order to obtain the shortest path to the target, a general framework is provided to model the problem of target detection when the target's location is probabilistically known. To this end, two algorithms are proposed: Path planning and target detection. The path planning algorithm is based on Bayesian inference and the target detection is accomplished by means of a residual neural network (ResNet) trained on the image dataset captured by the drone as well as existing pictures and datasets on the web. Through simulation and experiment, the proposed path planning algorithm is compared with two benchmark algorithms. It is shown that the proposed algorithm significantly decreases the average time of the mission.

Abstract: We present a toolchain for solving path planning problems for concentric tube robots through obstacle fields. First, ellipsoidal sets representing the target area and obstacles are constructed from labelled point clouds. Then, the nonlinear and highly nonconvex optimal control problem is solved by introducing a homotopy on the obstacle positions where at one extreme of the parameter the obstacles are removed from the operating space, and at the other extreme they are located at their intended positions. We present a detailed example (with more than a thousand obstacles) from stereotactic neurosurgery with real-world data obtained from labelled MPRI scans. Keywords: optimal control, non-linear programming, homotopy methods, optimal path planning, concentric tube robots, stereotactic neurosurgery 1. INTRODUCTION by finding paths of connected voxels that the robot can traverse, subject to constraints on its curvature.

In this paper, we propose a modular navigation system that can be mounted on a regular powered wheelchair to assist disabled children and the elderly with autonomous mobility and shared-control features. The lack of independent mobility drastically affects an individual's mental and physical health making them feel less self-reliant, especially children with Cerebral Palsy and limited cognitive skills. To address this problem, we propose a comparatively inexpensive and modular system that uses a stereo camera to perform tasks such as path planning, obstacle avoidance, and collision detection in environments with narrow corridors. We avoid any major changes to the hardware of the wheelchair for an easy installation by replacing wheel encoders with a stereo camera for visual odometry. An open source software package, the Real-Time Appearance Based Mapping package, running on top of the Robot Operating System (ROS) allows us to perform visual SLAM that allows mapping and localizing itself in the environment. The path planning is performed by the move base package provided by ROS, which quickly and efficiently computes the path trajectory for the wheelchair. In this work, we present the design and development of the system along with its significant functionalities. Further, we report experimental results from a Gazebo simulation and real-world scenarios to prove the effectiveness of our proposed system with a compact form factor and a single stereo camera.

It is very easy to organize the various requirements and deadlines using traditional planning techniques, such as material requirements planning and its derivatives. They create a plan or schedule that seems logical and might actually function by applying a fixed logic to the vast amount of data. A typical computer-generated plan, however, may include elements that are unrealistic or outright impossible, such as having a work center commit to three or four times the amount of production that it can complete in a day. This is something that any experienced planner will tell you. The latest generation of so-called advanced planning and scheduling (APS) software, which trades off priorities and alternatives instead of using fixed logic to build a more workable plan that balances resources and material dates and amounts, overcomes many of these restrictions.

There has been increasing attention on planning model learning in classical planning. Most existing approaches, however, focus on learning planning models from structured data in symbolic representations. It is often difficult to obtain such structured data in real-world scenarios. Although a number of approaches have been developed for learning planning models from fully observed unstructured data (e.g., images), in many scenarios raw observations are often incomplete. In this paper, we provide a novel framework, \aType{Recplan}, for learning a transition model from partially observed raw image traces. More specifically, by considering the preceding and subsequent images in a trace, we learn the latent state representations of raw observations and then build a transition model based on such representations. Additionally, we propose a neural-network-based approach to learn a heuristic model that estimates the distance toward a given goal observation. Based on the learned transition model and heuristic model, we implement a classical planner for images. We exhibit empirically that our approach is more effective than a state-of-the-art approach of learning visual planning models in the environment with incomplete observations.

Recent years have witnessed substantial growth in adaptive traffic signal control (ATSC) methodologies that improve transportation network efficiency, especially in branches leveraging artificial intelligence based optimization and control algorithms such as reinforcement learning as well as conventional model predictive control. However, lack of cross-domain analysis and comparison of the effectiveness of applied methods in ATSC research limits our understanding of existing challenges and research directions. This chapter proposes a novel unified view of modern ATSCs to identify common ground as well as differences and shortcomings of existing methodologies with the ultimate goal to facilitate cross-fertilization and advance the state-of-the-art. The unified view applies the mathematical language of the Markov decision process, describes the process of controller design from both the world (problem) and solution modeling perspectives. The unified view also analyses systematic issues commonly ignored in existing studies and suggests future potential directions to resolve these issues.