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Object Recognition allows robots to identify and classify objects within their environment, crucial for tasks like manipulation and sorting.

Computer Vision enables robots to interpret and make decisions based on visual information from the surrounding environment, similar to human vision.

Neural Networks are computing systems inspired by biological neural networks, which can help robots in pattern recognition and decision making.

NLP allows robots to understand and respond to human language, facilitating better human-robot interaction and communication.

Machine Learning is a subset of AI that includes algorithms allowing robots to learn from and make predictions based on data, improving their tasks over time.

Reinforcement Learning involves robots learning to make decisions by receiving rewards or penalties for actions, often used in autonomous navigation.

ROS is an open-source framework providing services for hardware abstraction, device drivers, and more, fundamental for robot software development.

Energy Efficiency in Robotics is the pursuit of reducing the energy consumption of robots, crucial for operational cost reduction and sustainability.

Robotic Grasping involves the mechanical manipulation of objects by a robot, which requires perception, motion, and control strategies.

Haptic Feedback refers to the use of touch sensation and control to interact with computer applications or robots, enhancing the sense of touch.

Path Planning is a computational problem to find a sequence of valid configurations that moves a robot from the source to the destination.

Cognitive Robotics incorporates AI models to emulate human cognition, which helps robots in decision-making and problem-solving.

Motion Control is the process of moving parts of robots in a controlled manner, often involving feedback and computational algorithms to achieve desired positions.

Inverse Kinematics is the process of calculating the joint parameters necessary to move a robotic arm to a desired end position.

DAI focuses on the development of distributed solutions for complex problems, which in robotics helps with multi-agent coordination and robustness.

Cloud Robotics leverages cloud computing to augment robots' capabilities for storage, processing power, and shared learning.

Teleoperation refers to the remote control of a robot, allowing humans to manipulate and interact with environments from a distance.

Probabilistic Robotics applies the principles of probability theory to deal with the uncertainties in perception and control in robotics.

Multi-Robot Systems involve coordinating multiple robots to perform tasks more effectively than a single robot, requiring complex planning and communication.

Deep Learning is a class of machine learning based on learning data representations, used in robotics for complex tasks like image and speech recognition.

Evolutionary Robotics uses evolutionary computation to develop controllers and/or hardware for robots, optimizing them for complex environments.

Robotic Perception enables robots to understand and interpret sensory information, which is crucial for safely navigating and interacting with the world.

Actuators are components that enable robot movement by converting energy into mechanical motion, playing a key role in robot mobility.

Adaptive Control allows a robot's control strategies to adapt in real-time to changes in the robotic system or the environment.

HRI studies how humans and robots communicate and work together, aiming for cooperative and intuitive interactions.

Compliant Mechanisms are flexible systems that use elastic deformation to transfer forces and displacements, beneficial for safe human-robot interaction.

Behavioral Robotics uses AI to emulate and interpret complex animal behaviors, granting robots the ability to act in a more life-like and autonomous way.

Bio-inspired Robotics takes design cues from biological organisms, which aids in developing more efficient and adaptable robotic systems.

SLAM entails having a robot build a map of an unknown environment while simultaneously keeping track of their own location within it.

Swarm Robotics is inspired by the collective behavior of social insects and focuses on the coordinated control of large numbers of relatively simple robots.

Context Awareness refers to a robot's ability to sense, interpret, and respond accordingly to its environment, which is essential for adaptive behavior.

Genetic Algorithms are heuristic search and optimization techniques inspired by natural selection, used in robotics to optimize tasks and adapt to changes.

Sensor Fusion combines data from multiple sensors to build a more accurate, reliable, and comprehensive view of the environment.

Fuzzy Logic provides a way to arrive at decisions with imprecise and uncertain information, applicable in robotics for more adaptive and human-like decision making.

Cobots are designed to work alongside humans, offering assistance with safety and efficiency in mind, and designed to be easy to program and redeploy.

Quantum Computing has the potential to revolutionize robotics by enabling exponentially faster computation for data processing and problem-solving.

Autonomous Robots can perform tasks without human intervention by integrating sensors, control systems, and actuators.