Behavior Trees (BTs) were invented as a tool to enable modular AI in computer games, but have received an increasing amount of attention in the robotics community in the last decade. With rising demands on agent AI complexity, game programmers found that the Finite State Machines (FSM) that they used scaled poorly and were difficult to extend, adapt and reuse. In BTs, the state transition logic is not dispersed across the individual states, but organized in a hierarchical tree structure, with the states as leaves. This has a significant effect on modularity, which in turn simplifies both synthesis and analysis by humans and algorithms alike. These advantages are needed not only in game AI design, but also in robotics, as is evident from the research being done. In this paper we present a comprehensive survey of the topic of BTs in Artificial Intelligence and Robotic applications. The existing literature is described and categorized based on methods, application areas and contributions, and the paper is concluded with a list of open research challenges.
Last week, Sony and Carnegie Mellon University announced a collaboration "on artificial intelligence (AI) and robotics research." Usually, these announcements pretty much just end there, with the implication being that giant corporation X will support academic research institution Y by funding ongoing research or a string of new initiatives. This Sony/CMU announcement is a bit more exciting because of how specific it is: The project will be about food. Researchers will focus on defining the domain of food ordering, preparation, and delivery. Initially, they will build upon existing manipulation robots and mobile robots, and will plan on developing new domain-specific robots for predefined food preparation items and for mobility in a limited confined space.
Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next few months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. With all the hype about SpotMini recently, it's a good time to take a look back at another quadruped that Boston Dynamics helped develop. This system is the first of its kind that can automatically keep a cluttered room neat and tidy at a practical level, something that has been difficult to achieve using conventional robot system.
Sheer cliff faces present a traversal challenge for most wheeled robots on the market, but researchers at the University of Tokyo say they've developed a two-robot framework that works pretty reliably in their testing. In a newly published paper on the preprint server Arxiv.org "[We] propose a novel cooperative system for an Unmanned Aerial Vehicle (UAV) and an Unmanned Ground Vehicle (UGV) which utilizes the UAV not only as a flying sensor but also as a tether attachment device," the authors of the paper explain. "[It enhances] the poor traversability of the UGV by not only providing a wider range of scanning and mapping from the air, but also by allowing the UGV to climb steep terrains with the winding of the tether." The UGV is permanently attached via mechanized winch and cable to the UAV, a custom-made quadcopter with an Nvidia Jetson TX2 chipset, a flight controller, and a raft of sensors including a modular fisheye camera, time-of-flight sensor, inertial measurement unit (IMU), and laser sensor.