From High-Level Reasoning to Low-Level Robot Execution and Back∗
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Qualitative Representations for Robots: Papers from the AAAI Spring Symposium A Planner for Ambient Assisted Living: From High-Level Reasoning to Low-Level Robot Execution and Back∗ M. Di Rocco,1 S. Sathyakeerthy,1 J. Grosinger,1 F. Pecora,1 A. Saffiotti,1 M. Bonaccorsi,2 F. Cavallo,2 R. Limosani,2 A. Manzi,2 G. Teti,3 P. Dario,2 y z x Abstract static sensors and actuators deployed in the environment. Robot ecologies are a growing paradigm in which one The combination of the advanced motion and manipulation or several robotic systems are integrated into a smart capabilities afforded by robots, and the pervasive sensing environment. Robotic ecologies hold great promises for and actuation capabilities afforded by the smart environ- elderly assistance. Planning the activities of these sys- ment, often results in systems which are more robust, flex- tems, however, is not trivial, and requires consideration ible, and modular than a traditional monolithic robot. Be- of issues like temporal and information dependencies cause of this, robot ecologies hold great promises for ap- among different parts of the ecology, exogenous ac- plications in domestic, everyday environments like elderly tions, and multiple, dynamic goals. We describe a plan- assistance. ner able to cope with the above challenges. We show Different works in literature address the integration of in particular how this planner has been incorporated in closed-loop into a full robotic system that performs robots and smart environments to provide physical and cog- daily tasks in support of elderly people. The full robot nitive support in the field of ambient assisted living (AAL). ecology is deployed in a test apartment inside a real res- In (Schroeter et al. 2013), a companion robot is used to idential building, and it is currently undergoing an ex- cope with mild cognitive impairments. (Huijnen et al. 2011) tensive user evaluation. describes two projects devoted to developing platforms for assisting people in daily tasks such as reminding or enter- Abstract tainment. In (Lowet and Frank 2012) an inexpensive archi- Robot ecologies are a growing paradigm in which one tecture has been developed to provide a support similar to or several robotic systems are integrated into a smart the previous approaches. (Cavallo et al. 2013) presents a environment. Robotic ecologies hold great promises for multi function robot for physical and cognitive assistance elderly assistance. Planning the activities of these sys- at home. (Bedaf et al. 2013) and (Cousins 2011) describe an tems, however, is not trivial, and requires consideration infrastructures equipped with robotic manipulators to pro- of issues like temporal and information dependencies vide physical support to the users. Relevant results have also among different parts of the ecology, exogenous ac- been obtained with outdoor or office-like platforms (Ferri tions, and multiple, dynamic goals. We describe a plan- ner able to cope with the above challenges. We show et al. 2011; Kanda et al. 2009; Mizoguchi, Hiraisci, and in particular how this planner has been incorporated Nishiyama 1999). In most of these works, however, it is as- in closed-loop into a full robotic system that performs sumed that the service provided by the system consists of daily tasks in support of elderly people. The full robot a single task; where this is not the case, the form of rea- ecology is deployed in a test apartment inside a real res- soning employed to cope with multiple tasks is simplified idential building, and it is currently undergoing an ex- by not considering situations where there might be multiple tensive user evaluation. and concurrent goals. Furthermore, the above approaches do not consider multiple robots. 1 Introduction In this paper we present a planner for multi-robot, multi- Robotic ecologies (Saffiotti et al. 2008) are networked sys- task ecologies developed in the context of the EU project 1 tems composed of one or several robots that cooperate with Robot-Era. To the best of our knowledge, this is the first attempt to integrate an autonomous multi-robot system in a ∗ This work was funded by the EC Seventh Framework Pro- complex, smart environment in the service of elderly peo- gramme (FP7/2007-2013) grant agreement no. 288899 Robot-Era. y1 ple. This application domain introduces several important Center for Applied Autonomous Sensor Systems (AASS), new challenges and requirements to the planning system: Orebro¨ University, Sweden. [email protected] z2 (a) since we do not focus on one specific task, the planner The BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa, must cope with a variety of different, possibly concurrent Italy. [email protected] goals that can be posted any time; (b) because of the dy- x3Robotech srl, Italy. [email protected] Copyright c 2014, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. 1www.robot-era.eu 10 namic nature of the environment and of human interaction, Some work has addressed the issue of including re- the planner is committed to perform high-level reasoning sources into the planning problem. Some of these ap- that has to be further mapped into low-level (inter-)actions proaches (Kockemann,¨ Pecora, and Karlsson 2012; Fratini, that fulfill the on-going requirements; (d) the presence of Pecora, and Cesta 2008; Ghallab and Laruelle 1994) would a multi-robot system demands a form of coordination be- also be well suited for use in closed loop with actua- tween the units that can possibly perform heterogeneous ac- tion and perception, as they maintain a certain level of tions while receiving support from the smart environment; least-commitment with respect to the timing of plan exe- and (d) the intrinsic concurrency in a robot ecology rises cution. Nevertheless, they are neither defined nor evaluated challenges related to the usage of common resources, time as closed-loop planning systems. (Lemai and Ingrand 2004) synchronization, and causal and information dependencies. propose an extension of the IxTeT planner (Ghallab and To satisfy the above requirements, we control our robot Laruelle 1994) for closed loop execution monitoring with ecology using a configuration planner (M. Di Rocco et al. resources; however, their technique is only applied to single 2013) that is able to produce fine-grained plans for robotic robot navigation tasks. systems. The planner can model the causal, temporal, re- source and information dependencies between the sensing, 3 High-level reasoning computation, and actuation components in one or multiple Task planners typically reason about abstract causality: what robots. As we shall see, the planner has features which are actions should be performed in order to bring the system in geared toward the integration with low-level, physical robot a state where the goal is satisfied? To better close the gap execution in the real world. In this paper we show how the with low-level robot execution, our configuration planner configuration planner has been integrated in the full Robot- also reasons about finer-grained aspects that are relevant to Era robot ecology, starting from high-level reasoning and re- successful execution, like time, information, and resources. fining this down to low-level interaction between the devices Moreover, our planner operates in closed-loop: it continu- in the ecology. ously incorporates new sensor observations and goals dur- ing execution, and it adapts the current plan to take these 2 Related work into account — or, if adaptation is not possible, generates an Classical planning approaches make a number of assump- entirely new plan. tions that abstract away from the details of physical exe- Our approach is grounded on the notion of state variable cution in real, dynamic environments (Ghallab, Nau, and (SV), which models elements of the domain whose state in Traverso 2004). In particular, aspects of time, information, time is represented by a symbol. State variables, whose do- and resources are often ignored. By contrast, our work is mains are discrete sets, represent features of the environment grounded on temporal planning techniques, and enriches this we want to model as well as functionalities provided by paradigm with the ability to reason about resource, causal the ecology. The particular instantiation of a functionality, and information dependencies among the entities involved called activity, can either produce information (information in the plan. All these features are needed when orchestrat- output) or effects on the world, i.e., act on state variables ing networked robotic systems: the information flow plays representing the environment. We employ a (flexible) tem- a crucial role to coordinate the various units, while resource poral representation that allows us to restrict the occurrence constraints must be carefully taken into account since de- in time of a particular predicate over a SV. vices may share and exploit limited resources. The temporal To be executed, a functionality may need sources of in- aspect has a pivotal role, and the tasks involved may be sub- formation (information input) and consume resources. Our ject to deadlines. In addition, the concurrency of multiple framework takes into account reusable resources, i.e., re- goals emphasizes the role of synchronization between inter- sources that are fully available when not used by any func- acting functionalities. tionality. The information flow as well as the manipulation Today’s planners exhibit some of the above features. For of the world leads to the connection among different func- instance, a considerable amount of work has been done to tionalities during plan generation: these interactions and de- integrate metric time into planning (Knight et al. 2001; Do pendencies are modeled temporally through Allen’s Interval and Kambhampati 2003; Gerevini, Saetti, and Serina 2006; Algebra (Allen 1984). These are the thirteen possible tempo- Doherty, Kvarnstrom,¨ and Heintz 2009; Barreiro et al. 2012; ral relations between intervals, namely “before” (b), “meets” Eyerich, Mattmuller,¨ and Roger¨ 2009).