About this book:

*Deep Learning for Robot Perception* provides a comprehensive technical guide to building visual and multimodal understanding systems for autonomous mobile robots and manipulators. The book moves beyond generic computer vision by addressing the specific constraints of embodiment, such as the "tyranny of the real-time loop," battery power limitations, and the necessity of managing sensor noise like motion blur and rolling shutter. It establishes a foundational architecture consisting of four layers—sensing, processing, understanding, and acting—emphasizing that robot perception must provide actionable, probabilistic facts to downstream planners rather than just static classifications.

The text details a wide array of sensing modalities, including RGB and event cameras, LiDAR, radar, and IMUs, alongside the deep learning architectures used to process them. It covers the evolution from Convolutional Neural Networks (CNNs) to Vision Transformers (ViTs) and hybrid designs, explaining their application in 2D and 3D object detection, semantic segmentation, and multi-object tracking. A significant portion of the book is dedicated to the practical challenges of data, discussing dataset curation, the "sim-to-real" gap, and the use of self-supervised and contrastive learning to reduce the heavy reliance on human-annotated labels. It also explores spatial reasoning through scene graphs and the integration of learned perception with classical Simultaneous Localization and Mapping (SLAM).

A major theme of the book is the transition from research models to production-grade deployment. It provides "actionable recipes" for model compression techniques like quantization and pruning, as well as low-latency profiling on edge hardware such as GPUs and TPUs. The authors emphasize the importance of MLOps, uncertainty estimation, and "validation-in-the-loop" to ensure safety and robustness in unpredictable environments. By analyzing case studies in manipulation, navigation, and aerial systems, the book illustrates how architectural choices must be co-designed with a robot's mechanical constraints and safety protocols.

The final section looks toward the future of the field, highlighting the potential of foundation models for robotics and neuro-symbolic AI. It identifies persistent challenges in causal reasoning, lifelong learning, and the ethical implications of pervasive robot sensing. Ultimately, the book argues that true robotic autonomy requires a synergistic approach that marries the data-driven power of deep learning with the mathematical rigor of classical robotics, creating systems that are not only intelligent but also resilient, efficient, and safe for real-world interaction.

What You'll Find Inside:

• Multimodal sensor fusion of camera, LiDAR, radar, and IMU data is essential for robust perception in dynamic, unstructured environments.
• The book surveys modern architectures—from CNNs and Vision Transformers to hybrid models—guiding selection based on latency, accuracy, and hardware constraints.
• Practical deployment techniques such as model compression, quantization, edge inference on GPUs/TPUs, and ROS 2 integration enable real-time operation on resource‑constrained robots.
• Dataset curation, active learning, and data flywheels turn robot experience into a continuous improvement loop for perception models.
• Safety‑critical topics like uncertainty estimation, out‑of‑distribution robustness, validation‑in‑the‑loop, and graceful degradation ensure reliable autonomy.

Who's It For:

The target audience includes graduate students entering robotics perception, perception engineers deploying systems on real platforms, and researchers advancing the frontier; readers should have basic deep learning and linear algebra knowledge.