Bayesian Policy Optimization for Waste Crane With Garbage Inhomogeneity

• Published in: IEEE robotics and automation letters Vol. 5; no. 3; pp. 4533 - 4540
• Main Authors: Sasaki, Hikaru, Hirabayashi, Terushi, Kawabata, Kaoru, Onuki, Yukio, Matsubara, Takamitsu
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.07.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: AI-Based methods; automation technologies for smart cities; Bayes methods; Bayesian analysis; Combustion; Cranes; Flammability; Gaussian processes; Incineration; industrial robots; Inhomogeneity; Kernel; Mechanical systems; Nonhomogeneous media; Optimization; Outliers (statistics); Policies; Robust control; Task analysis; Waste disposal
• ISSN: 2377-3766; 2377-3766
• DOI: 10.1109/LRA.2020.3002204

The objective of this study is to develop a framework that can optimize control policies of a waste crane at a waste incineration plant through an autonomous trial and error manner. Since a waste crane is a massive mechanical system that moves slowly and takes several minutes to execute a task, obtaining data samples by executing tasks is very costly. Moreover, no sensors are available that can observe the state of the grasped flammable waste composed of various materials with different degrees of hardness and wetness. Therefore, the inhomogeneity of waste causes unpredictable fluctuation in the crane's task performance. To cope with these problems, we propose a framework for optimizing the policy parameters of a parameterized control policy with Multi-Task Robust Bayesian Optimization (MTRBO). Our framework features the following two characteristics: (1) outlier robustness against garbage inhomogeneity and (2) sample reuse from previously solved tasks to enhance its sample efficiency. To investigate the effectiveness of our framework, we conducted experiments on garbage-scattering tasks with (i) a robot waste crane with pseudo-garbage and (ii) an actual waste crane at a waste incineration plant. Experimental results demonstrate that our framework robustly optimized the control policies of the garbage cranes, even with a much reduced amount of data under the influence of garbage inhomogeneity.