Journal
ROBOTICS AND AUTONOMOUS SYSTEMS
Volume 99, Issue -, Pages 1-16Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.robot.2017.10.002
Keywords
Object recognition; Action recognition; Assembly state estimation; Learning by demonstration
Funding
- National Science Foundation (NSF) [CISE/IIS 1231671/IIS 1427345]
- National Natural Science Foundation of China (NSFC) Grants [61328302, 61222310, U1613210]
- Open Research Project of the State Key Laboratory of Industrial Control Technology, Zhejiang University, China [ICT170314]
- Shenzhen Overseas High Level Talent (Peacock Plan) Program [KQTD20140630154026047]
Ask authors/readers for more resources
Acquiring robot assembly skills through human demonstration is an important research problem and can be used to quickly program robots in future manufacturing industries. To teach robots complex assembly skills, the robots should be able to recognize the objects (parts and tools) involved, the actions applied, and the effect of the actions on the parts. It is non-trivial to recognize the subtle assembly actions. To estimate the effect of the actions on the assembly part is also challenging due to the small part sizes. In this paper, using a RGB-D camera, we build a Portable Assembly Demonstration (PAD) system which can automatically recognize the objects (parts/tools) involved, the actions conducted and the assembly states characterizing the spatial relationship among the parts. The experiment results proved that this PAD system can generate a high level assembly script with decent accuracy in object and action recognition as well as assembly state estimation. The assembly script is successfully implemented on a Baxter robot. (c) 2017 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available