Artificial Intelligence for Robotics: Machine Learning, Computer Vision, and Autonomous Decision-Making for Intelligent Machines
Robotics has changed. The textbooks haven't.
Classical robotics books ignore deep learning. Deep learning books ignore embodiment and safety. You're left stitching together fifteen specialized texts - and still missing the integration.
This book is the fix.
Artificial Intelligence for Robotics
The complete discipline in one volume
400+ pages. 10 chapters. 30 figures. One unified engineering framework - built around "Aria," an autonomous warehouse robot whose design thread runs through every chapter.
What you'll master:
- Sensors & perception - calibration, fusion, computer vision, SLAM- Learning - deep learning, reinforcement learning, sim-to-real transfer
- Probabilistic reasoning - Kalman filters, POMDPs, uncertainty quantification
- Planning & control - A*, RRT*, MPC, behavior trees, motion planning
- Manipulation - grasping, dexterous control, diffusion policies, VLAs
- Foundation models - RT-2, OpenVLA, LLM-based task planning, grounding
- Safety & deployment - ISO 21448, assurance cases, MLOps, fleet management
Plus: 6 appendices, 50+ worked examples, 10 lab exercises, 250-entry glossary.
Why this book, why now?
- Current as of 2026 - covers the EU AI Act, UL 4600, latest VLAs- Integrated - classical and learning-based methods, side by side
- Production-oriented - every chapter ends with deployment considerations
- Mathematically honest - derivations included, not hand-waved
Who it's for:
Graduate students. Practicing engineers. ML researchers moving into physical systems. Technical leaders shipping autonomy into the real world.
The field is moving faster than it ever has.
Engineers who understand integration will build the robots of 2030. Engineers who only know algorithms will watch from the sidelines.
→ Order your copy today.
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