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Layla Croll, Laura Giles, Chris Black, Sandor Veres, Sean Anderson, Jonathan Aitken, Noel Sharkey, Mahnaz Arvaneh, Natalie Wood, Leonardo Stella, and Uziel Jaramillo-Avila

This course looked at current and future developments in the field of robotics that could shape different aspects of people’s daily lives. It built an understanding of the principles and fundamental challenges of designing robots including: Most FutureLearn courses run multiple times. Every run of a course has a set start date but you can join it and work through it after it starts. Find out more This course is open to anyone with an interest in robotics. It will be of particular interest to engineers, designers, technicians and enthusiasts who are keen to explore the role of robots in our economic and industrial future. You can use the hashtag #FLRoboFuture to talk about this course on social media.

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This course looked at current and future developments in the field of robotics that could shape different aspects of people’s daily lives. It built an understanding of the principles and fundamental challenges of designing robots including: Most FutureLearn courses run multiple times. Every run of a course has a set start date but you can join it and work through it after it starts. Find out more This course is open to anyone with an interest in robotics. It will be of particular interest to engineers, designers, technicians and enthusiasts who are keen to explore the role of robots in our economic and industrial future. You can use the hashtag #FLRoboFuture to talk about this course on social media.

Topics Covered

  • What a robot needs to be able to sense the world using drones, youBots and autonomous cars as examples.
  • Current and future developments in robotics and how and where they are being applied.
  • How robotics can take inspiration from nature looking at swarm robots and biomimetics.
  • How robots work with others for example, a robot surgeon and a robotic arm.

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Reviews summary

Overview of robotics future and applications

According to students, this course provides a positive, high-level overview of the exciting field of robotics and its future applications. Many learners found the content interesting and inspiring, particularly appreciating the coverage of topics like drones, autonomous cars, and biomimetics. It is often described as a good starting point for those new to the subject or enthusiasts looking for a broad understanding. However, some reviewers noted that the course can be too basic for those with technical backgrounds and lacks deep technical detail or practical application advice, leading to it feeling somewhat superficial for some.
Accessible and clear for newcomers.
"As someone completely new to robotics, this course was a perfect starting point. Easy to follow and understand."
"I recommend this course for enthusiasts or anyone just curious about the future of technology."
"It's a great introductory course, assumes no prior knowledge which was perfect for me."
"The explanations were clear and made complex ideas approachable for a layperson."
Content like drones and AI sparks interest.
"The sections on autonomous vehicles and drone technology were particularly fascinating and well-presented."
"Really enjoyed seeing the real-world examples of robotics in action, from surgery to nature-inspired designs."
"The course does a great job highlighting the cool and exciting potential of robots in our daily lives."
"Inspired me to learn more about specific areas like swarm robotics after seeing the examples."
Provides a comprehensive, high-level introduction.
"It gave me a really good overview of the different areas of robotics and its potential future."
"The course covers a wide range of topics, showing how robots are being applied across various fields."
"Learned about many different aspects of robotics I hadn't considered before, like biomimetics and swarm robots."
"Excellent introduction to the field of robotics and its impact on society."
Surface-level coverage disappointing for some.
"While the overview is nice, I was hoping for more technical details on how these robots actually work."
"Too basic for anyone with an engineering background; it felt more like a general interest lecture series."
"Could use more in-depth coverage on the engineering principles behind the examples shown."
"I found it frustratingly superficial, it mentions cool concepts but doesn't explain the 'how'."

Career center

Learners who complete Building a Future with Robots will develop knowledge and skills that may be useful to these careers:

Reading list

We've selected 50 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Building a Future with Robots.
This widely recognized and comprehensive textbook that provides a deep dive into the subject of language processing. It is suitable for both undergraduate and graduate-level courses and is considered a must-read for gaining a strong theoretical and applied understanding of NLP. The authors are leading researchers in the field, and the book is frequently updated to reflect advancements. The latest draft is available online.
The premier applied machine learning masterwork, taking practitioners through intuitive concept breakdowns, production-grade Scikit-Learn workflows, and deep neural architecture implementations.
The definitive benchmark textbook of modern artificial intelligence, rigorously unifying search algorithms, probabilistic reasoning, knowledge representation, and machine learning into an elegant, coherent agent-based framework.
A timeless mathematical masterpiece that provides an uncompromising Bayesian and probabilistic foundation for linear classifiers, graphical models, kernel methods, and sampling algorithms.
A monumental foundational text establishing the mathematical, algorithmic, and statistical principles of modern deep neural networks, from linear algebra and optimization to convolutional and sequence modeling architectures.
The definitive mathematical and theoretical canon of statistical learning, establishing the rigorous analytical foundations of regularized regression, tree ensembles, and high-dimensional inference.
This practical book dives into using transformers, the dominant architecture for many state-of-the-art NLP tasks, with the Hugging Face Transformers library. , It's ideal for data scientists and coders who want to learn how to build and scale models for various NLP applications using this popular library. It covers contemporary topics and is highly relevant for current NLP practice.
A distilled practitioner field guide outlining best practices for the entire machine learning lifecycle, from problem formulation and feature engineering to model deployment and monitoring.
Delivers an end-to-end systems architecture guide for building reliable production ML systems, addressing data engineering trade-offs, real-time feature computation, and model drift mitigation.
Guides engineers step-by-step through implementing, training, and fine-tuning complete generative transformer architectures in pure PyTorch, providing crystal-clear low-level insight into modern foundation models.
Presents the foundational treatise on Markov decision processes, temporal-difference learning, and policy gradient methods that underpin autonomous decision-making and modern post-training alignment workflows.
As a comprehensive handbook, this book covers a vast range of topics in robotics, from fundamentals to advanced research. It is an excellent reference for researchers and professionals, providing detailed insights into various subfields of robotics. While not a textbook for a single course, it is invaluable for deepening understanding and exploring contemporary topics.
Bridges linear algebra, multivariable calculus, vector geometry, and probability directly to fundamental machine learning algorithms like PCA, Gaussian mixture models, and support vector machines.
Takes a crucial look at the ethical considerations in the design and deployment of drones. It advocates for a value-sensitive design approach, considering human welfare, privacy, and environmental sustainability. This highly relevant book for contemporary discussions around drone technology and is essential for students and professionals involved in drone development and policy.
Examines deep learning techniques specifically for computer vision tasks, including CNNs, object detection, and image segmentation. It provides a comprehensive understanding of concepts and algorithms, making it helpful for practitioners and researchers in this area. It includes practical examples using TensorFlow and Keras.
Considered a classic in the field, this book delves deep into the geometry of multiple cameras and 3D reconstruction. It's essential for those looking to deepen their understanding of the mathematical foundations of computer vision, particularly for topics like autonomous driving and 3D mapping. It rigorous text often used at the graduate level.
This foundational text offers a comprehensive introduction to statistical methods in NLP. It covers the necessary theory and algorithms for building NLP tools, with a rigorous treatment of mathematical and linguistic foundations. While published in 1999, it remains highly relevant for understanding the statistical underpinnings of many NLP techniques and valuable reference for researchers and students alike.
Instructs data scientists on applied model evaluation, cross-validation, hyperparameter tuning, and pipeline construction through Scikit-Learn maintainers Andreas Müller and Sarah Guido.
Focusing on the engineering aspects, this book delves into the design and analysis of UAVs from a systems perspective. It covers conceptual and detailed design, including components like ground stations, power systems, and flight control. This valuable resource for those seeking to deepen their understanding of the technical intricacies of drone development and is often used in aerospace engineering programs.
Provides a comprehensive and rigorous treatment of computer vision, covering a wide range of topics from image formation to object recognition. It's suitable for advanced undergraduates and graduate students seeking a deep understanding of the field. It is often used as a primary textbook in university courses.
Provides a comprehensive introduction to deep learning, including its applications in computer vision. It combines theory with hands-on implementation using various frameworks. It's a valuable resource for understanding the deep learning methods used in modern computer vision.
Focuses on multi-rotor UAVs, covering their architecture, integration into airspace, mission planning, flight mechanics, and control. It includes practical elements like MATLAB/SIMULINK tutorials. It is suitable for advanced undergraduate and graduate students, as well as engineers in UAV R&D.
As drone usage increases, managing their integration into airspace is critical. introduces Unmanned Aircraft Systems Traffic Management (UTM), explaining how it facilitates safe and compliant operations. It explores the architecture, services, and challenges of UTM, making it essential reading for professionals and policymakers involved in aviation and drone operations.
Presents advanced computer vision methods, with a strong emphasis on recent machine and deep learning techniques. It covers topics like generative adversarial networks and deep reinforcement learning, suitable for researchers and advanced students.
Focusing on the algorithms behind mobile robot perception and control, this book is essential for understanding topics like localization, mapping, and state estimation in the presence of uncertainty. It is geared towards graduate students and researchers and is considered a seminal work in the field of mobile robotics.
Serves as a practical introduction to NLP with a strong focus on using the Natural Language Toolkit (NLTK) library in Python. It's excellent for beginners and those who want a hands-on approach to learning NLP concepts through programming examples. It's widely used as a textbook and provides a solid foundation for implementing basic NLP tasks.
Investigates the engineering realities of running automated ML pipelines in production, addressing data drift detection, automated retraining loops, telemetry infrastructure, and continuous validation.
Delivers battle-tested operational patterns for serving foundation models, addressing GPU cluster throughput, semantic caching, automated evaluation benchmarks, and continuous latency minimization.
Focuses on the mathematical and statistical underpinnings of computer vision, providing a solid theoretical foundation. It's well-suited for those who want to understand the principles behind various algorithms and models. It can be a valuable reference for researchers and graduate students.
Details an applied architectural methodology for running high-performance open-weights models on local workstations, tackling quantization fidelity, memory bandwidth limits, and prompt assembly trade-offs.
Provides a comprehensive overview of robot motion planning, covering theoretical foundations, algorithms, and practical implementations. It is suitable for graduate students and researchers and key reference for anyone working on how robots navigate and move in complex environments.
Details cloud-native DevOps architectures for orchestrating automated machine learning lifecycles, integrating CI/CD triggers, model registry synchronization, and automated hyperparameter sweep workers.
Covers deep learning concepts and their application to computer vision, explaining how computers can understand images. It explores image classification, object detection, and generative models. It's a good resource for those looking to apply deep learning to vision tasks.
This textbook offers a technical perspective on NLP, synthesizing classical methods with contemporary machine learning techniques. , It covers topics from basic textual analysis to structured representations and semantic analysis, making it suitable for advanced undergraduate and graduate courses. It requires a background in programming and college-level mathematics.
Delves into the advanced concepts of UAV autonomy, exploring topics like intelligent control, mission planning, and future applications. It is geared towards researchers and those with a strong foundational understanding of UAV systems looking to explore cutting-edge developments.
Offers a rigorous theoretical foundation for reasoning under uncertainty, combining graph theory and probability distributions to model complex high-dimensional dependencies across autonomous intelligent systems.
Provides a practical, step-by-step guide for implementing drone technology in AEC workflows. It covers data acquisition, processing, and integration with existing project data. It's a valuable resource for professionals and students in these specific fields looking to leverage drones.
While not exclusively about drones, this official FAA handbook provides fundamental knowledge of aeronautics, which is essential for understanding how drones fly and the regulations surrounding their operation, particularly in the US. It crucial resource for anyone pursuing drone pilot certification.
Offers a critical perspective on the use of armed drones and their impact. It raises important questions about accountability, civilian casualties, and the psychological effects on drone operators. It's a key text for understanding the controversies and ethical debates surrounding military drone applications.
Provides a rigorous mathematical treatment of robotic manipulation, focusing on the kinematics, dynamics, and control of robot arms. It is aimed at graduate students and researchers with a strong mathematical background and is considered a foundational text for theoretical robotics.
Provides a thought-provoking examination of the impact of drone technology on international relations, security, and surveillance. It explores the ethical and political implications of drone warfare and proliferation. This valuable resource for those interested in the broader societal impact of drones and serves as excellent additional reading for courses discussing the ethical dimensions of technology.
Offers a detailed survey and analysis of various feature description and machine vision methods. It provides a taxonomy for understanding different approaches and helps develop intuition about how these methods work. It's a valuable reference for researchers and those seeking a deeper understanding of feature analysis.
Offers a practical, hands-on introduction to computer vision using the widely popular OpenCV library and Python. It's excellent for beginners and those who want to implement computer vision techniques. It covers various practical applications and useful reference for coding projects.
Synthesizes the five foundational tribes of machine learning into a unified conceptual framework, illuminating how symbolists, connectionists, evolutionaries, Bayesians, and analogizers approach intelligence.
Presents the foundational treatise on Markov decision processes, temporal-difference learning, and policy gradient methods that underpin autonomous decision-making and modern post-training alignment.
Offers a concise overview of key issues surrounding drones, including their use in counterterrorism, proliferation, and surveillance. It's written in an accessible question-and-answer format, making it a good starting point for gaining a broad understanding of the topic.

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