TY - CHAP
T1 - Improved Task Planning through Failure Anticipation in Human-Robot Collaboration
AU - Izquierdo-Badiola, Silvia
AU - Canal, Gerard
AU - Rizzo, Carlos
AU - Alenyà, Guillem
N1 - Funding Information:
1Eurecat, Centre Tecnològic de Catalunya, Robotics and Automation Unit, Barcelona, Spain {silvia.izquierdo, carlos.rizzo}@eurecat.org 2Department of Informatics, King’s College London, United Kingdom gerard.canal@kcl.ac.uk 3Institut de Robòtica i Informàtica Industrial, CSIC-UPC, Barcelona, Spain {sizquierdo,galenya}@iri.upc.edu *S. Izquierdo is a fellow of Eurecat’s Vicente López PhD grant program. This work was financially supported by the Catalan Government through the funding grant ACCIÓ-Eurecat, and partially supported by MCIN/ AEI /10.13039/501100011033 under the project CHLOE-GRAPH (PID2020-118649RB-l00); by MCIN/ AEI /10.13039/501100011033 and by the European Union NextGenerationEU/PRTR under the project COHERENT (PCI2020-120718-2). Gerard Canal has been partially supported by EPSRC grant THuMP (EP/R033722/1) and by the Royal Academy of Engineering and the Office of the Chief Science Adviser for National Security under the UK Intelligence Community Postdoctoral Research Fellowship programme.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Human-Robot Collaboration (HRC) has become a major trend in robotics in recent years with the idea of combining the strengths from both humans and robots. In order to share the work to be done, many task planning approaches have been implemented. However, they don't fully satisfy the required adaptability in human-robot collaborative tasks, with most approaches not considering neither the state of the human partner nor the possibility of adapting the collaborative plan during execution or even anticipating failures. In this paper, we present a planning system for human-robot collaborative plans that takes into account the agents' states and deals with unforeseen human behaviour, by replanning in anticipation when the human state changes to prevent action failure. The human state is defined in terms of capacity, knowledge and motivation. The system has been implemented in a standardised environment using the Planning Domain Definition Language (PDDL) and the modular ROSPlan framework, and we have validated the approach in multiple simulation settings. Our results show that using the human model fosters an appropriate task allocation while allowing failure anticipation, replanning in time to prevent it.
AB - Human-Robot Collaboration (HRC) has become a major trend in robotics in recent years with the idea of combining the strengths from both humans and robots. In order to share the work to be done, many task planning approaches have been implemented. However, they don't fully satisfy the required adaptability in human-robot collaborative tasks, with most approaches not considering neither the state of the human partner nor the possibility of adapting the collaborative plan during execution or even anticipating failures. In this paper, we present a planning system for human-robot collaborative plans that takes into account the agents' states and deals with unforeseen human behaviour, by replanning in anticipation when the human state changes to prevent action failure. The human state is defined in terms of capacity, knowledge and motivation. The system has been implemented in a standardised environment using the Planning Domain Definition Language (PDDL) and the modular ROSPlan framework, and we have validated the approach in multiple simulation settings. Our results show that using the human model fosters an appropriate task allocation while allowing failure anticipation, replanning in time to prevent it.
UR - http://www.scopus.com/inward/record.url?scp=85136335151&partnerID=8YFLogxK
U2 - 10.1109/ICRA46639.2022.9812236
DO - 10.1109/ICRA46639.2022.9812236
M3 - Conference paper
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 7875
EP - 7880
BT - 2022 IEEE International Conference on Robotics and Automation, ICRA 2022
ER -