When a robot executes a task, it is necessary to model the relationship among its body, target objects, tools, and environment, and to control its body to realize the target state. However, it is difficult to model them using classical methods if the relationship is complex. In addition, when the relationship changes with time, it is necessary to deal with the temporal changes … [Read more...] about Deep Predictive Model Learning With Parametric Bias: Handling Modeling Difficulties and Temporal Model Changes
Predictive models
Models of Human Behavior for Human–Robot Interaction and Automated Driving: How Accurate Do the Models of Human Behavior Need to Be?
There are many examples of cases where access to improved models of human behavior and cognition has allowed the creation of robots that can better interact with humans, and not least in road vehicle automation, this is a rapidly growing area of research. Human–robot interaction (HRI) therefore provides an important applied setting for human behavior modeling—but given the vast … [Read more...] about Models of Human Behavior for Human–Robot Interaction and Automated Driving: How Accurate Do the Models of Human Behavior Need to Be?
Uncertainty-Aware Trajectory Planning: Using Uncertainty Quantification and Propagation in Traversability Prediction of Planetary Rovers
In an extreme environment, such as Mars or a volcanic area, mobile robots have been used in scientific missions, or as precursors for a future manned mission. The robot called a planetary exploration rover is managed by a space-qualified, radiation-hardened, and low-clock onboard computer, and autonomously travels over challenging terrain. For more about this … [Read more...] about Uncertainty-Aware Trajectory Planning: Using Uncertainty Quantification and Propagation in Traversability Prediction of Planetary Rovers
Bionic Underwater Vehicle: A Data-Driven Disturbance Rejection Control Framework
Disturbances caused by unknown dynamics and environmental factors render the automatic control of underwater vehicles extremely challenging. These effects are complex, time varying, and difficult to model accurately, leading to possible instability in the control process. This article focuses on the disturbance rejection problem of an underactuated bionic underwater vehicle … [Read more...] about Bionic Underwater Vehicle: A Data-Driven Disturbance Rejection Control Framework
