Treffer: End-User Programming of Low-and High-Level Actions for Robotic Task Planning

Title:
End-User Programming of Low-and High-Level Actions for Robotic Task Planning
Contributors:
Artificial Intelligence and Robotics (Marvin), Laboratoire d'Informatique de Grenoble (LIG), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])
Source:
IEEE International Symposium on Robot and Human Interactive Communication, Oct 2019, New Delhi, India
Publisher Information:
CCSD, 2019.
Publication Year:
2019
Collection:
collection:UGA
collection:CNRS
collection:INPG
collection:LIG
collection:LIG_SIC_MARVIN
collection:UGA-COMUE
collection:LIG_SIDCH
collection:TEST-UGA
Subject Geographic:
Original Identifier:
HAL: hal-02408664
Document Type:
Konferenz conferenceObject<br />Conference papers
Language:
English
Rights:
info:eu-repo/semantics/OpenAccess
Accession Number:
edshal.hal.02408664v1
Database:
HAL

Weitere Informationen

Programming robots for general purpose applications is extremely challenging due to the great diversity of end-user tasks ranging from manufacturing environments to personal homes. Recent work has focused on enabling end-users to program robots using Programming by Demonstration. However, teaching robots new actions from scratch that can be reused for unseen tasks remains a difficult challenge and is generally left up to robotic experts. We propose iRoPro, an interactive Robot Programming framework that allows end-users to teach robots new actions from scratch and reuse them with a task planner. In this work we provide a system implementation on a two-armed Baxter robot that (i) allows simultaneous teaching of low-and high-level actions by demonstration, (ii) includes a user interface for action creation with condition inference and modification, and (iii) allows creating and solving previously unseen problems using a task planner for the robot to execute in real-time. We evaluate the generalisation power of the system on six benchmark tasks and show how taught actions can be easily reused for complex tasks. We further demonstrate its usability with a user study (N=21), where users completed eight tasks to teach the robot new actions that are reused with a task planner. The study demonstrates that users with any programming level and educational background can easily learn and use the system.