Multi-robot task acquisition through sparse coordination

In this paper, we consider several autonomous robots with separate tasks that require coordination, but not a coupling at every decision step. We assume that each robot separately acquires its task, possibly from different providers. We address the problem of multiple robots incrementally acquiring tasks that require their sparse-coordination. To this end, we present an approach to provide tasks to multiple robots, represented as sequences, conditionals, and loops of sensing and actuation primitives. Our approach leverages principles from sparse-coordination to acquire and represent these joint-robot plans compactly. Specifically, each primitive has associated preconditions and effects, and robots can condition on the state of one another. Robots share their state externally using a common domain language. The complete sparse-coordination framework runs on several robots. We report on experiments carried out with a Baxter manipulator and a CoBot mobile service robot.

[1]  Manuela M. Veloso,et al.  Learning environmental knowledge from task-based human-robot dialog , 2013, 2013 IEEE International Conference on Robotics and Automation.

[2]  Maja J. Mataric,et al.  Sold!: auction methods for multirobot coordination , 2002, IEEE Trans. Robotics Autom..

[3]  Manuela M. Veloso,et al.  An interactive approach for situated task specification through verbal instructions , 2014, AAMAS.

[4]  Richard Fikes,et al.  STRIPS: A New Approach to the Application of Theorem Proving to Problem Solving , 1971, IJCAI.

[5]  Nikos A. Vlassis,et al.  Utile Coordination: Learning Interdependencies Among Cooperative Agents , 2005, CIG.

[6]  Manuela M. Veloso,et al.  Decentralized MDPs with sparse interactions , 2011, Artif. Intell..

[7]  Manuela M. Veloso,et al.  Exploiting factored representations for decentralized execution in multiagent teams , 2007, AAMAS '07.

[8]  Peter Stone,et al.  Layered learning in multiagent systems - a winning approach to robotic soccer , 2000, Intelligent robotics and autonomous agents.

[9]  Kevin Leyton-Brown,et al.  Action-Graph Games , 2011, Games Econ. Behav..

[10]  Manuela M. Veloso,et al.  Interactive robot task training through dialog and demonstration , 2007, 2007 2nd ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[11]  Manuela M. Veloso,et al.  Localization and navigation of the CoBots over long-term deployments , 2013, Int. J. Robotics Res..

[12]  L. Iocchi,et al.  Petri Net Plans A Framework for Collaboration and Coordination in Multi-Robot Systems , 2013 .

[13]  Daniele Nardi,et al.  Teaching Robots Parametrized Executable Plans Through Spoken Interaction , 2015, AAMAS.

[14]  Matthias Scheutz,et al.  What to do and how to do it: Translating natural language directives into temporal and dynamic logic representation for goal management and action execution , 2009, 2009 IEEE International Conference on Robotics and Automation.

[15]  Milind Tambe,et al.  Agent Architectures for Flexible, Practical Teamwork , 1997, AAAI/IAAI.

[16]  Craig A. Knoblock,et al.  PDDL-the planning domain definition language , 1998 .

[17]  Lynne E. Parker,et al.  ALLIANCE: an architecture for fault tolerant multirobot cooperation , 1998, IEEE Trans. Robotics Autom..

[18]  Guido Bugmann,et al.  Personal Robot Training via Natural-Language Instructions. , 2001 .

[19]  John E. Laird,et al.  Learning Goal-Oriented Hierarchical Tasks from Situated Interactive Instruction , 2014, AAAI.

[20]  Pedro U. Lima,et al.  Petri Net Plans , 2011, Autonomous Agents and Multi-Agent Systems.

[21]  Yunyi Jia,et al.  Teaching Robots New Actions through Natural Language Instructions , 2014, The 23rd IEEE International Symposium on Robot and Human Interactive Communication.