Artificial Intelligence (AI) Algorithm and Models for Embodied Agents (Robots and Drones)

Embodied agents, including robots and drones, rely on a myriad of artificial intelligence (AI) algorithms and models to interact with and navigate their environments. This abstract provides an overview of the essential AI components used in building such agents. These include computer vision techniques like convolutional neural networks and object tracking, sensor fusion to integrate data from diverse sensors, reinforcement learning for task learning, simultaneous localization and mapping (SLAM) for environment understanding, path planning and control algorithms, natural language processing for human interaction, 3D perception, transfer learning, imitation learning, self-supervised learning, vision-based reinforcement learning, and more. The selection of these techniques depends on the specific application and hardware capabilities of the embodied agent. By harnessing these AI tools, embodied agents can perform a wide range of tasks, from autonomous navigation to complex object manipulation and human interaction, making them valuable assets in various fields such as robotics, autonomous vehicles, and industrial automation. This chapter explores the pivotal role of artificial intelligence (AI) algorithms and models in the development and empowerment of embodied agents. Embodied agents represent a transformative paradigm in AI, enabling autonomous entities to interact with and adapt to real or simulated environments. The fusion of AI and embodiment offers boundless possibilities across various domains, including robotics, autonomous vehicles, virtual assistants, and digital avatars. Also, the chapter examines the essential AI techniques that underpin the functionality of embodied agents.