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Introduction to Robot Learning

Robot learning is a simple idea: teach robots to improve from data and experience instead of hand‑coding every behavior. In practice, that means using examples (videos, sensor data, demonstrations) and feedback to help a robot get better at tasks like picking, placing, pushing, or walking.

Why now? Two trends make this possible: modern machine learning is very good at finding patterns, and robotics datasets are becoming easier to collect and share. Together, they let us move from classical “write the physics and the controller” approaches to learning‑based methods that adapt from data.

For example, a robot arm can learn to grasp a block after watching and imitating human demonstrations (imitation learning), or it can learn by trying actions and getting rewards for progress (reinforcement learning). Over time, the same ideas scale to many tasks and even different robot bodies.

> By the end of this unit, you will:

> - Understand in plain terms what “robot learning” means and why it’s useful
> - See concrete examples of learning from demonstrations and from trial‑and‑error
> - Know what we’ll build toward in the next units and how LeRobot fits in

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Some History

Autonomous robotics holds the premise of relieving humans from repetitive, tiring or dangerous manual tasks. Consequently, the field of robotics has been widely studied since its first inception in the 1950s. Lately, advancements in Machine Learning (ML) have sparked the development of a relatively new class of methods used to tackle robotics problems, leveraging large amounts of data and computation rather than human expertise and modeling skills to develop autonomous systems.

> Some context…

> The 1950s saw the birth of both artificial intelligence and robotics as distinct fields. It’s taken nearly 70 years for these fields to converge in meaningful ways through robot learning!

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The Future of Robotics

The frontier of robotics research is indeed increasingly moving away from classical model-based control paradigm, embracing the advancements made in ML, aiming to unlock:

1. Monolithic perception-to-action control pipelines
2. Multi-modal data-driven feature extraction strategies*
3. Reduced reliance on precise models of the world
4. A better positioning to benefit from the growing availability of open robotics data

> Key Insight: This shift represents a fundamental change in how we think about robotics - from engineering precise solutions to learning adaptive behaviors from data.

While central problems in manipulation (using robotic arms to interact with objects), locomotion (moving through environments), and whole-body control (coordinating complex robotic systems) have traditionally demanded deep knowledge of rigid-body dynamics, contact modeling, and planning under uncertainty, recent results seem to indicate learning can prove just as effective as explicit modeling, sparking interest in the field of robot learning. This interest can be largely justified considering the significant challenges related to deriving accurate mathematical models of how robots interact with complex, unpredictable environments.

Moreover, since end-to-end learning on ever-growing collections of text and image data has historically been at the core of the development of foundation models (large-scale AI systems like GPT and CLIP that can understand and reason across multiple types of data), deriving robotics methods grounded in learning appears particularly consequential, especially as the number of openly available robotics datasets continues to grow.

Robotics is, at its core, an inherently multidisciplinary field, requiring a wide range of expertise in both software and hardware. The integration of learning-based techniques further broadens this spectrum of skills, raising the bar for both research and practical applications.

Update on GitHub