The application of artificial intelligence and control theory to robotic systems to enable autonomous navigation, manipulation, perception, and task execution. AI-driven robotics control integrates reinforcement learning for policy optimisation, computer vision for scene perception, motion planning for collision-free trajectory generation, and sensor fusion for robust state estimation — operating in real time under uncertainty and safety constraints.
Semantic Classification
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Key Characteristics
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Integrates perception, planning, and control in closed loops
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Employs model-based and model-free control strategies
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Handles dynamic environments and uncertainty
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Enables learning from demonstrations and human feedback
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Ensures safety constraints and collision avoidance
Overview
Robotics Control integrates artificial intelligence with robotic systems to enable autonomous navigation, manipulation, perception, and task execution. AI-driven control employs reinforcement learning for policy optimization, computer vision for perception, motion planning algorithms, and sensor fusion. Key challenges include real-time decision-making, handling uncertainty, sim-to-real transfer, and safety assurance. Modern approaches leverage deep learning for visuomotor control, imitation learning from demonstrations, and meta-learning for rapid adaptation to new tasks and environments.
Related Concepts
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References
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Levine, S. et al. (2016). End-to-End Training of Deep Visuomotor Policies. JMLR 17(39), 1-40.
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Kober, J. et al. (2013). Reinforcement learning in robotics: A survey. International Journal of Robotics Research, 32(11), 1238-1274.
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Finn, C. et al. (2017). Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks. ICML 2017.