AI, neuroscience, and robotics go very well together to research bio-inspired AI. I’m starting from scratch in robotics, so I was looking for a good book to get started. After scouting the web and Reddit, I found many recommendations about the Springer Handbook of Robotic, so I decided to give it a go.

First off, this book is an absolute unit. It’s a massive, comprehensive tome that aims to cover the entire field of robotics. My initial thought when looking at the number of pages (2000+) was, “Holy Sh*t, how am I ever going to get through this?” The key, I quickly learned, is patience. You eat an elephant one bite at a time they say! This isn’t a book you read cover-to-cover in a week. It’s a reference, a guide… well an handbook. I found the best approach was to read the foundational sections and then hop around to the chapters that really piqued my interest.

Now, let’s talk about the math. If you’re like me and your advanced calculus is a bit rusty, you might see the formulas and feel a wave of panic. Don’t. While the book is definitely math-heavy, I realized I didn’t need to derive every equation myself. My goal was to understand the concepts behind the kinematics, dynamics, and control systems. I read through these parts more quickly, focusing on the explanations. The beauty of a handbook like this is that the formulas are always there, waiting for you to come back and reference them when you’re working on a specific problem and need to get into the nitty-gritty.

What I really appreciated was the book’s structure. The editors did a fantastic job of laying everything out logically. It starts with the fundamentals, the core principles that every roboticist needs to know. Once you have that base, you can venture into the more specialized sections on everything from robot hands and mobile robots to flying and even neurorobotics. It’s incredibly well-organized, which makes navigating its sheer size much more manageable.

Overall, it’s an outstanding book. My only small gripe is that it can be a bit verbose at times, with some sections feeling a bit repetitive. But that’s a minor point for a book of this size.

Section breakdown:

• Part A: Robotics Foundations
• Part B: Design
• Part C: Sensing and Perception
• Part D: Manipulation and Interfaces
• Part E: Moving in the Environment
• Part F: Robots at Work
• Part G: Robots and Humans

The real highlight for me was the final part of the book. The sections on biologically-inspired robotics and the connection between robotics and neuroscience were absolutely fascinating. I’ll admit I’m biased here, as my main interests lie in AI and neuroscience, so this was right up my alley. I was also surprised by how great some of the research and ideas were from a long time ago, these early pioneers were truly ahead of their time.

If you’re looking for a single resource to guide you through the vast landscape of robotics, you can’t go wrong by picking the Springer Handbook of Robotics. Just be prepared to give it the time it deserves (it took me a solid two months). It’s not a light read, but it’s one of the most rewarding ones you’ll find. I’m now more excited than ever to dive into building robots!

Study Notes

Table of Content

• Kinematics

Kinematics

Kinematics in Robotics: Kinematics focuses on the motion of robotic mechanisms without considering the forces or torques that cause the motion.

Coordinate Frames: A coordinate frame consists of an origin (O) and three mutually orthogonal basis vectors (Ox, Oy, Oz).

Fixed vs. Moving Frames: The fixed frame is considered stationary, while the moving frame changes position.

Pose Representation: The pose of a body is defined as its position and orientation relative to a reference frame. The minimum number of coordinates required to locate a body in Euclidean space is six.

Rigid-Body Displacements: Combines translations and rotations to describe the movement of a rigid body.

Orientation and Rotation: Rigid-body kinematics requires defining both position and orientation. Unlike position, which uses three coordinates, representing orientation requires more complex methods such as:

• Rotation Matrices: Efficient representation of orientation using a 3×3 orthogonal matrix. It is particularly useful for combining multiple rotations via matrix multiplication.
• Euler Angles: Represents orientation using three angles corresponding to the sequential rotations about the coordinate axes (e.g., Z-Y-X order). Euler angles can suffer from singularities (gimbal lock).
• Angle-Axis Representation: Uses a unit vector to indicate the rotation axis and an angle to indicate the amount of rotation. Efficient and less prone to singularities compared to Euler angles.
• Quaternions: Four parameters representing rotations without singularities, making them useful for 3D rotations. Used extensively in robotics and computer graphics.
• Homogeneous Transformations: Combine rotation matrices and translation vectors into a single 4×4 matrix, allowing compact notation for position and orientation.
• Screw Transformations: Represents combined rotation and translation using a screw axis and pitch.
• Exponential Map: Represents rotations and transformations as matrix exponentials. particularly useful for continuous transformations.
• Plücker Coordinates: Efficient way to represent lines in space using six coordinates. Used for spatial velocity and kinematic transformations.