End-User Programming of Low- and High-Level Actions for Robotic Task Planning
Ying Siu Liang, Damien Pellier, Humbert Fiorino, and Sylvie Pesty

TL;DR
This paper introduces iRoPro, an interactive framework enabling end-users to teach robots new actions from scratch through demonstration, facilitating reuse in complex tasks with a user-friendly interface and real-time task planning.
Contribution
The paper presents a novel system that allows non-expert users to teach and reuse robot actions via demonstration, with integrated condition inference and real-time task planning.
Findings
System successfully generalizes to six benchmark tasks.
Users with varied backgrounds can easily learn and operate the system.
Taught actions are effectively reused for complex, unseen tasks.
Abstract
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…
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Taxonomy
TopicsRobot Manipulation and Learning · Teaching and Learning Programming · Social Robot Interaction and HRI
