Posts and Articles about AI techs and computer science
Robot Learning is the field utilizing Machine Learning and Deep Learning methods for training AI models to perform new physical skills on a robotic platform. The same term is also used for a new type of job titles, namely robot learning engineer, which is commonly focused on Imitation and Interactive learning of robotic skills from recorded datasets. This last formulation also features key words…
For any physically-intelligent mobile robot that operates autonomously in the real world, successful navigation is a critical capability. This requirement of the topic is also highly relevant across various sectors, including logistics, autonomous driving, and search-and-rescue applications. Furthermore, advanced robotic tasks, such as object manipulation, often depend on the foundational…
*Reinforcement Learning (RL) represents a powerful framework for solving sequential decision-making problems in dynamic environments across diverse domains, such as control of robots or optimization of profit. However, its practical implementation requires navigating a variety of software packages, encompassing deep learning libraries (e.g., TensorFlow, PyTorch, JAX/Flax), environment frameworks…
*Imagine teaching a robot to cook, drive a car, or even manage a stock portfolio. A natural starting point is often to have the machine learn by simply copying how a human expert performs the task – observing their actions and mimicking their decisions. This approach, where machines learn from demonstration, is surprisingly similar to how we humans learn ourselves. Think about how children learn…
*Deep Reinforcement Learning is a branch of machine learning, gaining rapid traction recently as an efficient approach to skills acquisition by machines, across diverse fields of applications. From optimizing advertising placement to enabling robotic manipulation and through refining Large Language Models responses, its potential is predicted to be immense. The field encompasses a rich variety of…
*MujoCo is a physics simulator for robotics research developed by Google DeepMind and written in C++ with a Python API. The advantage of using MujoCo is due to its various implemented models along with full dynamic and physics properties, such as friction, inertia, elasticity, etc. This realism allows researchers to rigorously test reinforcement learning algorithms in simulations before…
*An overview of the most prominent imitation learning methods with tests on a grid environment* *Reinforcement learning is one branch of machine learning concerned with learning by guidance of scalar signals (rewards); in contrast to supervised learning, which needs full labels of the target variable.* Read more on Medium ... So, buckle up! and let's dive through imitation learning, from behavior…
Recently, (yet) another new neural network structure was proposed. Namely, Kolmogorov-Arnold Network (KAN). Soon this new structure attracted a lot of attention, and for good reason: interpretability. For what current Multi Layers Preceptron (MLPs) networks lack is a way to make sense of the network predictions. Magic isn't involved; we need to know how the learning is done, so we can improve,…
As a student or knowledge worker, time management is essential for achieving success. However, organizing one's schedule can be challenging, for instance one is faced with the problem of distributing work and rest times in optimal time windows. To address this issue, analyzing previous working schedules of an individual may provide useful recommendations for him. Read more… (7 min remaining to…
*After spending some years studying and using deep learning, I always suffered from the difficulty of debugging errors, or setting hyperparameters. As a researcher this can not only waste additional time, but also money and resources. In this article, we will demonstrate how traditional rule-based methods have a hidden edge (beside simplicity) in solving complex problems that require automation.*…