Artificial Intelligence for Robotics

Explore how reactive layers in robotics use perceptual schemas to drive actions via stimulus-response notation and schema theory to build modular object-oriented behaviors without planning.

Explore how affordances shape perception and how innate releasing mechanisms drive simple, stimulus-driven behaviors in robotics, via perceptual schemas, fixed action patterns, and implicit chaining.

Explore how multiple robot behaviors coordinate via a coordination function, blending concurrent behaviors with cooperative and competitive methods, and manage sequences through finite state and scripts.

Explore subsumption architecture for behavioral coordination in robotics, comparing suppression and inhibition in layered competence, with case studies on obstacle avoidance and the 1994 ground vehicle competition.

Explore how to explicitly string together temporal sequences of robot behaviors using finite state machines and scripts, capturing initialization, transitions, and exceptions in robotics.

Explore robot locomotion by comparing wheels and tracks with legged biomimetic motion; explain skid steering, Ackerman's steering, omni directional wheels, and non-harmonic concepts, plus dynamic and static balance in gait.

Explore simultaneous localization and mapping (slam) and how probabilistic methods, Kalman filters, and Monte-Carlo localization fuse localization with map building. Master frontier and graph-based exploration strategies.

Learn how robots acquire knowledge through unsupervised, supervised, reinforcement, and semi-supervised learning, using neural networks, support vector machines, Q-learning, and genetic algorithms. Address overfitting and architecture considerations.

Explore how multiagent systems enable robot teams to coordinate, reconfigure, and perform tasks from coverage to exploration, using five common team types and social entropy metrics.

Explore human-robot interaction as a joint cognitive system across physical, cognitive, and social-emotional domains, including uncanny valley considerations. Learn how people and robots share roles, emotions, and communication.

Explore how natural language processing distinguishes speech recognition, language understanding, and language generation, and examine multimodal communication, semantics, and the role of context in robot interaction.

Explore the ethical implications of robotics, including operational morality, functional morality, and full moral agency. Learn about designer responsibility, professional ethics, and top-down versus bottom-up ethical frameworks.