Supporting Semantic Capture during Kinesthetic Teaching of Collaborative Industrial Robots

Industrial robot systems being deployed today do not contain domain knowledge to aid robot operators in setup and operational use. To gather such knowledge in a robot context requires mechanisms for entering and capturing semantic data, that will gradually build a working vocabulary while interacting with environment and operators, for bootstrapping system knowledge and ensuring data collection over time. This paper presents a prototype user interface, assisting kinesthetic teaching of a collaborative industrial robot, that allows for capturing semantic information while working with the robot in day-to-day use. A graphical user interface with natural language processing builds a working vocabulary of the environment while modifying and/or creating robot programs. A simple demonstration illustrates the approach.

[1]  Henk Nijmeijer,et al.  Robot Programming by Demonstration , 2010, SIMPAR.

[2]  Brett Browning,et al.  A survey of robot learning from demonstration , 2009, Robotics Auton. Syst..

[3]  Carme Torras,et al.  A robot learning from demonstration framework to perform force-based manipulation tasks , 2013, Intelligent Service Robotics.

[4]  Manuel Lopes,et al.  Relational activity processes for modeling concurrent cooperation , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[5]  Elin Anna Topp,et al.  From Demonstrations to Skills for High-Level Programming of Industrial Robots , 2016, AAAI Fall Symposia.

[6]  Oussama Khatib,et al.  From Bot to Bot: Using a Chat Bot to Synthesize Robot Motion , 2016, AAAI Fall Symposia.

[7]  Pierre Nugues,et al.  A High-Performance Syntactic and Semantic Dependency Parser , 2010, COLING.

[8]  Jacek Malec,et al.  Knowledge-based instruction of manipulation tasks for industrial robotics , 2015 .

[9]  Heni Ben Amor,et al.  Sparse Latent Space Policy Search , 2016, AAAI.

[10]  Daniel Gildea,et al.  The Proposition Bank: An Annotated Corpus of Semantic Roles , 2005, CL.

[11]  Jacek Malec,et al.  Making Robotic Sense of Incomplete Human Instructions in High-Level Programming for Industrial Robotic Assembly , 2017, AAAI Workshops.

[12]  David L. Roberts,et al.  A Need for Speed: Adapting Agent Action Speed to Improve Task Learning from Non-Expert Humans , 2016, AAMAS.

[13]  Anne-Françoise Cutting-Decelle,et al.  ISO 15531 MANDATE: A Product-process-resource based Approach for Managing Modularity in Production Management , 2007, Concurr. Eng. Res. Appl..

[14]  Sergey Levine,et al.  One-shot learning of manipulation skills with online dynamics adaptation and neural network priors , 2015, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[15]  Klas Nilsson,et al.  Declarative-knowledge-based reconfiguration of automation systems using a blackboard architecture , 2011, SCAI.

[16]  Andreas Stolt,et al.  From High-Level Task Descriptions to Executable Robot Code , 2014, IEEE Conf. on Intelligent Systems.

[17]  Sylvain Calinon,et al.  A tutorial on task-parameterized movement learning and retrieval , 2015, Intelligent Service Robotics.