8 accepted papers
Imitation learning through a demonstration interface is expected to learn policies for robot automation from intuitive human demonstrations. However, due to the differences in human and robot movement characteristics, a human expert might unintentionally demonstrate an action that the robot cannot e
Partial Automation (PA) with intelligent support systems has been introduced in industrial machinery and advanced automobiles to reduce the burden of long hours of human operation. Under PA, operators perform manual operations (providing actions) and operations that switch to automatic/manual mode (
Robust imitation learning using disturbance injections overcomes issues of limited variation in demonstrations. However, these methods assume demonstrations are optimal, and that policy stabilization can be learned via simple augmentations. In real-world scenarios, demonstrations are often of divers…
The environments of such large industrial machines as waste cranes in waste incineration plants are often weakly observable, where little information about the environ-mental state is contained in the observations due to technical difficulty or maintenance cost (e.g., no sensors for observing the st…
Scenarios requiring humans to choose from multiple seemingly optimal actions are commonplace, however standard imitation learning often fails to capture this behavior. Instead, an over-reliance on replicating expert actions induces inflexible and unstable policies, leading to poor generalizability i…
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, obta
In this letter, we consider the problem of inner-shape estimation of objects covered with soft materials, e.g., pastries wrapped in paper or vinyl, water bottles covered with shock-absorbing fabrics, or human bodies dressed in clothes. Due to the softness of the covered materials, tactile informatio
In this paper, we present a novel policy search reinforcement learning algorithm that can deal with multimodality in control policies based on Gaussian processes. Our approach employs Overlapping Mixtures of Gaussian Processes (OMGPs) for a control policy, in which all the GPs in the mixture are glo…