Robot learning

Key takeaways

Robot learning belongs to engineering; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Robot learning to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Robot learning from memory before moving on to harder problems.

Reference excerpt

Robot learning is a research field at the intersection of machine learning and robotics. It studies techniques allowing a robot to acquire novel skills or adapt to its environment through learning algorithms. The embodiment of the robot, situated in a physical embedding, provides at the same time specific difficulties (e.g. high-dimensionality, real time constraints for collecting data and learning) and opportunities for guiding the learning process (e.g. sensorimotor synergies, motor primitives). Example of skills that are targeted by learning algorithms include sensorimotor skills such as locomotion, grasping, active object categorization, as well as interactive skills such as joint manipulation of an object with a human peer, and linguistic skills such as the grounded and situated meaning of human language. Learning can happen either through autonomous self-exploration or through guidance from a human teacher, like for example in robot learning by imitation. Robot learning can be closely related to adaptive control, reinforcement learning as well as developmental robotics which considers the problem of autonomous lifelong acquisition of repertoires of skills. While machine learning is frequently used by computer vision algorithms employed in the context of robotics, these applications are usually not referred to as "robot learning".

Imitation learning

Many research groups are developing techniques where robots learn by imitating. This includes various techniques for learning from demonstration (sometimes also referred to as "programming by demonstration") and observational learning.

Sharing learned skills and knowledge

In Tellex's "Million Object Challenge", the goal is robots that learn how to spot and handle simple items and upload their data to the cloud to allow other robots to analyze and use the information. RoboBrain is a knowledge engine for robots which can be freely accessed by any device wishing to carry out a task. The database gathers new information about tasks as robots perform them, by searching the Internet, interpreting natural language text, images, and videos, object recognition as well as interaction. The project is led by Ashutosh Saxena at Stanford University. RoboEarth is a project that has been described as a "World Wide Web for robots" − it is a network and database repository where robots can share information and learn from each other and a cloud for outsourcing heavy computation tasks. The project brings together researchers from five major universities in Germany, the Netherlands and Spain and is backed by the European Union. Google Research, DeepMind, and Google X have decided to allow their robots share their experiences.

Vision-language-action model Research groups and companies are developing vision-language-action models, foundation models that allow robotic control through the combination of vision and language. Google DeepMind, Figure AI and Hugging Face are actively working on that.

See also Cognitive robotics – Subfield of robotics Developmental robotics – Field of scientific study Evolutionary robotics – Embodied approach to artificial intelligence Philosophical ethology#History – Field of multidisciplinary research Comparison of machine learning software List of robotics software

References

External links IEEE RAS Technical Committee on Robot Learning (official IEEE website) IEEE RAS Technical Committee on Robot Learning (TC members website) Robot Learning at the Max Planck Institute for Intelligent Systems and the Technical University Darmstadt Robot Learning at the Computational Learning and Motor Control lab Archived 2021-01-24 at the Wayback Machine Humanoid Robot Learning at the Advanced Telecommunication Research Center (ATR) Archived 2011-08-30 at the Wayback Machine (in English and Japanese) Learning Algorithms and Systems Laboratory at EPFL (LASA) Robot Learning at the Cognitive Robotics Lab of Juergen Schmidhuber at IDSIA and Technical University of Munich The Humanoid Project: Peter Nordin, Chalmers University of Technology Inria and Ensta ParisTech FLOWERS team, France: Autonomous lifelong learning in developmental robotics CITEC at University of Bielefeld, Germany Asada Laboratory, Department of Adaptive Machine Systems, Graduate School of Engineering, Osaka University, Japan The Laboratory for Perceptual Robotics Archived 2020-06-14 at the Wayback Machine, University of Massachusetts Amherst Amherst, USA Centre for Robotics and Neural Systems, Plymouth University Plymouth, United Kingdom Robot Learning Lab at Carnegie Mellon University Project Learning Humanoid Robots at University of Bonn Skilligent Robot Learning and Behavior Coordination System (commercial product) Robot Learning class at Cornell University Robot Learning and Interaction Lab Archived 2015-12-17 at the Wayback Machine at Italian Institute of Technology Reinforcement learning for robotics Archived 2018-10-08 at the Wayback Machine at Delft University of Technology

Worked examples

Example 1 — a first encounter with Robot learning

Start with the simplest possible case. Write down what Robot learning claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In engineering, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Robot learning before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Robot learning ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Robot learning

In research: Robot learning appears in engineering research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Robot learning in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Robot learning is common in secondary-school and first-year university syllabi. It links to neighbouring topics Learning, Machine learning, Robot control, so understanding it makes those chapters shorter.
In everyday life: Look for Robot learning outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

How to study Robot learning in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Robot learning means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Robot learning out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Robot learning in simple terms?

Robot learning is a research field at the intersection of machine learning and robotics. It studies techniques allowing a robot to acquire novel skills or adapt to its environment through learning algorithms.

Why does Robot learning matter?

Because it connects several engineering ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Robot learning?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Robot learning.