Paper Info
Title
Teaching a robot to use electric tools with regrasp planning
Abstract
This study presents a straightforward method to teach robots to use tools. Teaching robots is crucial in quickly deploying and reconfiguring robots in next-generation factories. Conventional methods require third-party systems like wearable devices or complicated vision system to capture, analyse, and map human grasps, motion, and tool poses to robots. These systems assume lots of experience from their users. Unlike the conventional methods, this study does not involve learning human motion and skills. Instead, it only learns the object goal poses from the human user whilst employs regrasp planning to generate robot motion. The method is most suitable for a robot to learn the usage of electric tools that can be operated by simply switching on and off. The proposed method is validated using a dual-arm robot with hand-mounted cameras and several tools. Experimental results show that the proposed method is robust, feasible, and simple to teach robots. It can find a collision-free and kino-dynamic feasible grasp sequences and motion trajectories when the goal pose is reachable. The method allows the robot to automatically choose placements or handover considering the surrounding environment as intermediate states to change the pose of the tool and use tools following human demonstrations.
Year
DOI
Venue
2019
10.1049/trit.2018.1062
CAAI Transactions on Intelligence Technology
Keywords
Field
DocType
mobile robots,learning (artificial intelligence),motion control,path planning,cameras,robot vision,manipulators,dexterous manipulators
GRASP,Machine vision,Computer science,Human motion,Human–computer interaction,Robot motion,Wearable technology,Robot,Handover
Journal
Volume
Issue
ISSN
4
1
2468-6557
Citations 
PageRank 
References 
2
0.37
0
Authors
5
Name
Order
Citations
PageRank
Mohamed Raessa131.73
Daniel Sánchez220.37
Wan Weiwei312736.02
Damien Petit4103.29
Kensuke Harada51967172.97