# robot learning (blogs) — RSS Amplifier Recent posts from the 1 feeds in the RSS Amplifier directory that cover robot learning. Page: Feed: --- ## [(untitled)](https://engyasin.github.io/posts/a-8-months-study-plan-to-prepare-for-robot-learning-engineer-position/) _2026-02-09 · Yasin Yousif · Robot Learning by Example_ 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… ## [(untitled)](https://engyasin.github.io/posts/navigation-for-mobile-robots-in-ros-test-case-of-housekeeper-robot/) _2025-12-01 · Yasin Yousif · Robot Learning by Example_ 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… ## [(untitled)](https://engyasin.github.io/posts/reinforcement-learning-with-bells-and-whistles/) _2025-08-17 · Yasin Yousif · Robot Learning by Example_ \*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… ## [(untitled)](https://engyasin.github.io/posts/the-complete-guide-to-maximum-entropy-inverse-reinforcement-learning/) _2025-07-02 · Yasin Yousif · Robot Learning by Example_ \*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… ## [(untitled)](https://engyasin.github.io/posts/the-reinforcement-learning-algorithmic-landscape/) _2025-05-11 · Yasin Yousif · Robot Learning by Example_ \*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… ## [(untitled)](https://engyasin.github.io/posts/immerse-yourself-in-reinforcement-learning-and-robotics-with-mujoco/) _2025-02-05 · Yasin Yousif · Robot Learning by Example_ \*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… ## [(untitled)](https://engyasin.github.io/posts/hands-on-imitation-learning/) _2024-09-07 · Yasin Yousif · Robot Learning by Example_ \*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…