Welcome to our monthly digest, where you can catch up with any AIhub stories you may have missed, peruse the latest news, recap recent events, and more. This month, we hear about the latest research on soccer ball detection, learn about energy-based transformers, find out about memory traces in reinforcement learning, and explore some potential solutions to the problems with conference reviewing. Issues with the peer-review process, and pertaining to conferences in particular, are often discussed among authors, reviewers and conference chairs alike. However, coming up with potential solutions to the problem has proved challenging. Jaeho told us more in this interview .

RoboCup is an international scientific initiative with the goal of advancing the state of the art of intelligent robots, AI and automation. The annual RoboCup event took place from 15-21 July in Salvador, Brazil. The Logistics League forms part of the Industrial League and is an application-driven league inspired by the industrial scenario of a smart factory. Ahead of the Brazil meeting, we spoke with three key members of the league to find out more. Alexander Ferrein is a RoboCup Trustee overseeing the Industrial League, and Till Hofmann and Wataru Uemura are Logistics League Executive Committee members.

The RoboCup Logistics League is a RoboCup competition in a smart factory scenario that has focused on task planning, job scheduling, and multi-agent coordination. The focus on production logistics allowed teams to develop highly competitive strategies, but also meant that some recent developments in the context of smart manufacturing are not reflected in the competition, weakening its relevance over the years. In this paper, we describe the vision for the RoboCup Smart Manufacturing League, a new competition designed as a larger smart manufacturing scenario, reflecting all the major aspects of a modern factory. It will consist of several tracks that are initially independent but gradually combined into one smart manufacturing scenario. The new tracks will cover industrial robotics challenges such as assembly, human-robot collaboration, and humanoid robotics, but also retain a focus on production logistics. We expect the reenvisioned competition to be more attractive to newcomers and well-tried teams, while also shifting the focus to current and future challenges of industrial robotics.

This year, RoboCup will be taking place from 22-28 June as a fully remote event with RoboCup competitions and activities taking place all over the world. The RoboCup Logistics League (RCLL) is a sub-league of the RoboCup Industrial League. It focuses on in-factory logistics applications. The goal is for a team of autonomous robots to assemble products on demand, using a set of production machines. Each team comprises up to three autonomous robots which can produce using seven machines.

Smart factories are on the verge of becoming the new industrial paradigm, wherein optimization permeates all aspects of production, from concept generation to sales. To fully pursue this paradigm, flexibility in the production means as well as in their timely organization is of paramount importance. AI is planning a major role in this transition, but the scenarios encountered in practice might be challenging for current tools. Task planning is one example where AI enables more efficient and flexible operation through an online automated adaptation and rescheduling of the activities to cope with new operational constraints and demands. In this paper we present SMarTplan, a task planner specifically conceived to deal with real-world scenarios in the emerging smart factory paradigm. Including both special-purpose and general-purpose algorithms, SMarTplan is based on current automated reasoning technology and it is designed to tackle complex application domains. In particular, we show its effectiveness on a logistic scenario, by comparing its specialized version with the general purpose one, and extending the comparison to other state-of-the-art task planners.

Industries are on the brink of widely accepting a new paradigm for organizing production by having autonomous robots manage in-factory processes. This transition from static process chains towards more automation and autonomy poses new challenges in terms of, e.g., efficiency of production processes. The RoboCup Logistics League (RCLL) has been proposed as a realistic testbed to study the above mentioned problem at a manageable scale. In RCLL, teams of robots manage and optimize the material flow according to dynamic orders in a simplified factory environment. In particular, robots have to transport workpieces among several machines scattered around the factory shop floor. Each machine performs a specific processing step, orders that denote the products which must be assembled with these operations are posted at run-time and require quick planning and scheduling. Orders also come with a delivery time window, therefore introducing a temporal component into the problem. Though there exist successful heuristic approaches to solve the underlying planning and scheduling problems, a disadvantage of these methods is that they provide no guarantees about the quality of the solution. A promising solution to this problem is offered by the recently emerging field of Optimization Modulo Theories (OMT), where Satisfiability Modulo Theories (SMT) solving is extended with optimization functionalities. In this paper, we present an approach that combines bounded model checking and optimization to generate optimal controllers for multi-robot systems. In particular, using the RoboCup Logistics League as a testbed, we build formal models for robot motions, production processes, and for order schedules, deadlines and rewards. We then encode the synthesis problem as a linear mixed-integer problem and employ Optimization Modulo Theories to synthesize controllers with optimality guarantees.