Modification of Kohonen Rule for Vehicle Path Planing by Behavioral Cloning

The problem of path generation for the autonomous vehicle in environments with infinite number obstacles is considered. Generally, the problem is known in the literature as the path planning. This chapter treated that problem using the algorithm, named MKBC, which is based on the behavioral cloning and Kohonen rule. In the behavioral cloning, the system learns from control traces of a human operator. Kohonen rule connected with the weighting coefficients, while the MKBC algorithm does not use the weighting values as values from the previous time, but permanentlly uses the training values as weighting values. That is something which enables an intelligent system to learn from the examples (operator\u27s demonstrations) to control a vehicle in the process of the obstacles avoiding, like the human operator does. Like that, the very important MKBC characteristic is the symplicity. The MKBC simplicity is something which is so obviously, specialy according to the RBF neural network and the machine learnig algorithm which is used the previously. Following the MKBC given context the problem narrow passage avoiding and the goal position reaching fundamentally is observed. Namely, defining if ? then rule, according to the named cases is treated as destroying of the consistency of the methodology. In that sense, using MKBC neural network the solution was found. A the end, the autonomous vehicle mathematical model which is given by nonlinear equations describing a 12 state dynamical system is used and in that case the MKBC algorithm is applied successfully. Eventually, as it has been illustrated the previously, the advantage of the entire methodology lies in the fact that a complete path of the vehicle can be defined off-line, without using sophisticated symbolical models of obstacles. These are facts that MKBC algorithm and the given methodology substantially differ from the others. In the next phase it is expected to confirm results in on ? line simulation process. Key words: vehicle path planning, behavioral cloning, cloning success, obstacle avoiding, machine learning, Kohonen rule, neural network, Shark dynamical model

Theoretical Interpretations and Applications of Radial Basis Function Networks

Medical applications usually used Radial Basis Function Networks just as Artificial Neural Networks. However, RBFNs are Knowledge-Based Networks that can be interpreted in several way: Artificial Neural Networks, Regularization Networks, Support Vector Machines, Wavelet Networks, Fuzzy Controllers, Kernel Estimators, Instanced-Based Learners. A survey of their interpretations and of their corresponding learning algorithms is provided as well as a brief survey on dynamic learning algorithms. RBFNs' interpretations can suggest applications that are particularly interesting in medical domains

On the Utility of Koopman Operator Theory in Learning Dexterous Manipulation Skills

Despite impressive dexterous manipulation capabilities enabled by learning-based approaches, we are yet to witness widespread adoption beyond well-resourced laboratories. This is likely due to practical limitations, such as significant computational burden, inscrutable learned behaviors, sensitivity to initialization, and the considerable technical expertise required for implementation. In this work, we investigate the utility of Koopman operator theory in alleviating these limitations. Koopman operators are simple yet powerful control-theoretic structures to represent complex nonlinear dynamics as linear systems in higher dimensions. Motivated by the fact that complex nonlinear dynamics underlie dexterous manipulation, we develop a Koopman operator-based imitation learning framework to learn the desired motions of both the robotic hand and the object simultaneously. We show that Koopman operators are surprisingly effective for dexterous manipulation and offer a number of unique benefits. Notably, policies can be learned analytically, drastically reducing computation burden and eliminating sensitivity to initialization and the need for painstaking hyperparameter optimization. Our experiments reveal that a Koopman operator-based approach can perform comparably to state-of-the-art imitation learning algorithms in terms of success rate and sample efficiency, while being an order of magnitude faster. Policy videos can be viewed at https://sites.google.com/view/kodex-corl.Comment: This work has been accepted for an oral presentation at CORL 202

The disappearing human–machine divide

In this article a look is taken at some of the different ways in which the human–machine divide is rapidly disappearing. In each case the technical basis is described and then some of the implications are also considered. In particular results from experiments are discussed in terms of their meaning and application possibilities. The article is written from the perspective of scientific experimentation opening up realistic possibilities to be faced in the future, rather than giving conclusive comments. In each case consideration is also given to some of the philosophical questions that arise

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available