Many enhancements to Monte-Carlo Tree Search (MCTS) have been proposed over almost two decades of general game playing and other artificial intelligence research. However, our ability to characterise and understand which variants work well or poorly in which games is still lacking. This paper describes work on an initial dataset that we have built to make progress towards such an understanding: 268,386 plays among 61 different agents across 1494 distinct games. We describe a preliminary analysis and work on training predictive models on this dataset, as well as lessons learned and future plans for a new and improved version of the dataset.

Planning at execution time has been shown to dramatically improve performance for agents in both single-agent and multi-agent settings. A well-known family of approaches to planning at execution time are AlphaZero and its variants, which use Monte Carlo Tree Search together with a neural network that guides the search by predicting state values and action probabilities. AlphaZero trains these networks by minimizing a planning loss that makes the value prediction match the episode return, and the policy prediction at the root of the search tree match the output of the full tree expansion. AlphaZero has been applied to both single-agent environments (such as Sokoban) and multi-agent environments (such as chess and Go) with great success. In this paper, we explore an intriguing question: In single-agent environments, can we outperform AlphaZero by directly maximizing the episode score instead of minimizing this planning loss, while leaving the MCTS algorithm and neural architecture unchanged? To directly maximize the episode score, we use evolution strategies, a family of algorithms for zeroth-order blackbox optimization. Our experiments indicate that, across multiple environments, directly maximizing the episode score outperforms minimizing the planning loss.

Industrial manipulators have extensively collaborated with human operators to execute tasks, e.g., disassembly of end-of-use products, in intelligent remanufacturing. A safety task execution requires real-time path planning for the manipulator's end-effector to autonomously avoid human operators. This is even more challenging when the end-effector needs to follow a planned path while avoiding the collision between the manipulator body and human operators, which is usually computationally expensive and limits real-time application. This paper proposes an efficient hybrid motion planning algorithm that consists of an A$^*$ algorithm and an online manipulator reconfiguration mechanism (OMRM) to tackle such challenges in task and configuration spaces respectively. The A$^*$ algorithm is first leveraged to plan the shortest collision-free path of the end-effector in task space. When the manipulator body is risky to the human operator, our OMRM then selects an alternative joint configuration with minimum reconfiguration effort from a database to assist the manipulator to follow the planned path and avoid the human operator simultaneously. The database of manipulator reconfiguration establishes the relationship between the task and configuration space offline using forward kinematics, and is able to provide multiple reconfiguration candidates for a desired end-effector's position. The proposed new hybrid algorithm plans safe manipulator motion during the whole task execution. Extensive numerical and experimental studies, as well as comparison studies between the proposed one and the state-of-the-art ones, have been conducted to validate the proposed motion planning algorithm.

Autonomous mobile robots (AMRs) have been a rapidly expanding research topic for the past decade. Unlike their counterpart, the automated guided vehicle (AGV), AMRs can make decisions and do not need any previously installed infrastructure to navigate. Recent technological developments in hardware and software have made them more feasible, especially in warehouse environments. Traditionally, most wasted warehouse expenses come from the logistics of moving material from one point to another, and is exhaustive for humans to continuously walk those distances while carrying a load. Here, AMRs can help by working with humans to cut down the time and effort of these repetitive tasks, improving performance and reducing the fatigue of their human collaborators. This literature review covers the recent developments in AMR technology including hardware, robotic control, and system control. This paper also discusses examples of current AMR producers, their robots, and the software that is used to control them. We conclude with future research topics and where we see AMRs developing in the warehouse environment.

The ability to traverse an unknown environment is crucial for autonomous robot operations. However, due to the limited sensing capabilities and system constraints, approaching this problem with a single robot agent can be slow, costly, and unsafe. For example, in planetary exploration missions, the wear on the wheels of a rover from abrasive terrain should be minimized at all costs as reparations are infeasible. On the other hand, utilizing a scouting robot such as a micro aerial vehicle (MAV) has the potential to reduce wear and time costs and increasing safety of a follower robot. This work proposes a novel cooperative IPP framework that allows a scout (e.g., an MAV) to efficiently explore the minimum-cost-path for a follower (e.g., a rover) to reach the goal. We derive theoretic guarantees for our algorithm, and prove that the algorithm always terminates, always finds the optimal path if it exists, and terminates early when the found path is shown to be optimal or infeasible. We show in thorough experimental evaluation that the guarantees hold in practice, and that our algorithm is 22.5% quicker to find the optimal path and 15% quicker to terminate compared to existing methods.

This article introduces a novel method for converting 3D voxel maps, commonly utilized by robots for localization and navigation, into 2D occupancy maps that can be used for more computationally efficient large-scale navigation, both in the sense of computation time and memory usage. The main aim is to effectively integrate the distinct mapping advantages of 2D and 3D maps to enable efficient path planning for both unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). The proposed method uses the free space representation in the UFOMap mapping solution to generate 2D occupancy maps with height and slope information. In the process of 3D to 2D map conversion, the proposed method conducts safety checks and eliminates free spaces in the map with dimensions (in the height axis) lower than the robot's safety margins. This allows an aerial or ground robot to navigate safely, relying primarily on the 2D map generated by the method. Additionally, the method extracts height and slope data from the 3D voxel map. The slope data identifies areas too steep for a ground robot to traverse, marking them as occupied, thus enabling a more accurate representation of the terrain for ground robots. The height data is utilized to convert paths generated using the 2D map into paths in 3D space for both UAVs and UGVs. The effectiveness of the proposed method is evaluated in two different environments.

Safe autonomous navigation in unknown environments remains a critical challenge for robots with limited sensing capabilities. While safety-critical control techniques, such as Control Barrier Functions (CBFs), have been proposed to ensure safety, their effectiveness relies on the assumption that the robot has complete knowledge of its surroundings. In reality, robots often operate with restricted field-of-view and finite sensing range, which can lead to collisions with unknown obstacles if the planning algorithm is agnostic to these limitations. To address this issue, we introduce the visibility-aware RRT* algorithm that combines sampling-based planning with CBFs to generate safe and efficient global reference paths in partially unknown environments. The algorithm incorporates a collision avoidance CBF and a novel visibility CBF, which guarantees that the robot remains within locally collision-free regions, enabling timely detection and avoidance of unknown obstacles. We conduct extensive experiments interfacing the path planners with two different safety-critical controllers, wherein our method outperforms all other compared baselines across both safety and efficiency aspects.

Goal recognition design aims to make limited modifications to decision-making environments with the goal of making it easier to infer the goals of agents acting within those environments. Although various research efforts have been made in goal recognition design, existing approaches are computationally demanding and often assume that agents are (near-)optimal in their decision-making. To address these limitations, we introduce a data-driven approach to goal recognition design that can account for agents with general behavioral models. Following existing literature, we use worst-case distinctiveness($\textit{wcd}$) as a measure of the difficulty in inferring the goal of an agent in a decision-making environment. Our approach begins by training a machine learning model to predict the $\textit{wcd}$ for a given environment and the agent behavior model. We then propose a gradient-based optimization framework that accommodates various constraints to optimize decision-making environments for enhanced goal recognition. Through extensive simulations, we demonstrate that our approach outperforms existing methods in reducing $\textit{wcd}$ and enhancing runtime efficiency in conventional setup. Moreover, our approach also adapts to settings in which existing approaches do not apply, such as those involving flexible budget constraints, more complex environments, and suboptimal agent behavior. Finally, we have conducted human-subject experiments which confirm that our method can create environments that facilitate efficient goal recognition from real-world human decision-makers.

Learned construction heuristics for scheduling problems have become increasingly competitive with established solvers and heuristics in recent years. In particular, significant improvements have been observed in solution approaches using deep reinforcement learning (DRL). While much attention has been paid to the design of network architectures and training algorithms to achieve state-of-the-art results, little research has investigated the optimal use of trained DRL agents during inference. Our work is based on the hypothesis that, similar to search algorithms, the utilization of trained DRL agents should be dependent on the acceptable computational budget. We propose a simple yet effective parameterization, called $\delta$-sampling that manipulates the trained action vector to bias agent behavior towards exploration or exploitation during solution construction. By following this approach, we can achieve a more comprehensive coverage of the search space while still generating an acceptable number of solutions. In addition, we propose an algorithm for obtaining the optimal parameterization for such a given number of solutions and any given trained agent. Experiments extending existing training protocols for job shop scheduling problems with our inference method validate our hypothesis and result in the expected improvements of the generated solutions.

Spatial planning in cluttered environments is crucial for mobile systems, particularly agile quadrotors. Existing methods, both optimization-based and learning-based, often focus only on success rates in specific environments and lack a unified platform with tasks of varying difficulty. To address this, we introduce FlightBench, the first comprehensive open-source benchmark for 3D spatial planning on quadrotors, comparing classical optimization-based methods with emerging learning-based approaches. We also develop a suite of task difficulty metrics and evaluation metrics to quantify the characteristics of tasks and the performance of planning algorithms. Extensive experiments demonstrate the significant advantages of learning-based methods for high-speed flight and real-time planning, while highlighting the need for improvements in complex conditions, such as navigating large corners or dealing with view occlusion. We also conduct analytical experiments to justify the effectiveness of our proposed metrics. Additionally, we show that latency randomization effectively enhances performance in real-world deployments. The source code is available at \url{https://github.com/thu-uav/FlightBench}.