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 such as diffusion policy, or vision-action-language models. However, many other requirements also exist, such as robotic theory methods for navigation or arms control (like SLAM Filtering methods or Forward/Inverse Kinematic) as well as the usual software development skills like DevOps, Gitlab flow and Cloud computing. In this post we try to draw a full picture of these requirements, why and when they are needed. Additionally, suggested resources to study and projects to build for learning them are also mentioned along with an estimated time for doing that.

Read more…

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 (e.g., Gymnasium, Numpy), and hyperparameter optimization techniques and libraries. This post critically evaluates the common PyTorch, Gymnasium, and NumPy RL stack by comparing it to a faster alternative: JAX/Flax for both of the model training and simulation of environments. A Gridworld example evaluating both training speed and accuracy is utilized to test these packages. Additionally, we complement this example by a comprehensive tracking and monitoring of the training process using MLflow along with a thorough hyperparameters optimization via Optuna. The post concludes with a discussion of the results and final recommendations for optimal use cases of each of these packages.

Read more…

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 to speak, or how teenagers learn to drive: it's often through imitation. This fundamental concept forms the basis of a field in machine learning called Imitation Learning. In this post we will dive deep into a specific and popular method of Imitation Learning: Maximum Entropy Inverse Reinforcement Learning

Read more…

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 algorithms, spanning model-based and model-free techniques, policy gradients, and Q-learning methods. This dynamic landscape can be daunting for newcomers, even those with a foundation in supervised learning. In this post we provide a comprehensive overview, offering both a high-level perspective as well as a detailed examination of the underlying mathematical derivations and algorithms specifics. We’re also including full benchmarking results for all methods in both discrete and continuous environments, along with some unsolved questions and recommended readings for the interested reader.

Read more…

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 deployment, mitigating risks associated with real-world applications. Simulating exact replicas of robot manipulators becomes particularly valuable, enabling training in a safe virtual environment and seamless transition to production. Notable examples include open-source models for popular brands like ALOHA, FRANKA, and KUKA readily available within MujoCo.

Read more…

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…

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, fix, or extend it in an efficient manner. KANs take a significant step forward in this regard using addition operators, which have been proven to represent higher-order functions effectively.

Read more…

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…

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.

Read more…