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
Adaptation models
Online Tuning of Control Parameters for Off-Road Mobile Robots
This article addresses the problem of online adaptation of control parameters, dedicated to a path tracking problem in off-road conditions. Two approaches are offered to modify the tuning gain of a previously developed adaptive and predictive control law. The first approach is a deterministic method based on dynamic equations of the system, allowing the adaptation of the … [Read more...] about Online Tuning of Control Parameters for Off-Road Mobile Robots
Tumbling Robot Control Using Reinforcement Learning: An Adaptive Control Policy That Transfers Well to the Real World
Tumbling robots are simple platforms that are able to traverse large obstacles relative to their size, at the cost of being difficult to control. Existing control methods apply only a subset of possible robot motions and make the assumption of flat terrain. Reinforcement learning (RL) allows for the development of sophisticated control schemes that can adapt to diverse … [Read more...] about Tumbling Robot Control Using Reinforcement Learning: An Adaptive Control Policy That Transfers Well to the Real World
Toward Lifelong Learning for Industrial Defect Classification: A Proposed Framework
Automatic defect inspection is an important application for the development of smart factories in the era of Industry 4.0. It gathers data from production lines to train a model to automatically recognize certain types of defects. However, the defect types may vary in the production process, and it is difficult for the old model to adapt to new types of defects directly. … [Read more...] about Toward Lifelong Learning for Industrial Defect Classification: A Proposed Framework
