Energy and energy exchange govern interactions in the physical world. By explicitly considering the energy and power in a robotic system, many control and design problems become easier or more insightful than in a purely signal-based view.
Energy in Robotics presents a holistic, energy-based view of robotic systems. It examines the relevance of such energy considerations to robotics; starting from the fundamental aspects and proceeding to look at their practical application to robotic systems. Using the theory of Port-Hamiltonian Systems as a fundamental basis, it provides examples pertaining to energy measurement, passivity and safety. Control by interconnection covers the shaping and directing of energy inside controller algorithms, to achieve desired behaviour in a power-consistent manner. This idea of control over the energy ﬂow is extended to the physical domain. In their mathematical description and analysis, the boundary between controller and robot disappears and everything is an interconnected system, driven by energy exchange between its parts.
2. Energy in controlled physical systems
3. Control by interconnection
4. Control by physical interconnection
A. Energy control: proof
An Algorithmic Perspective on Imitation Learning provides the reader with an introduction to imitation learning. It covers the underlying assumptions, approaches, and how they relate; the rich set of algorithms developed to tackle the problem; and advice on effective tools and implementation.
An Algorithmic Perspective on Imitation Learning serves two audiences. First, it familiarizes machine learning experts with the challenges of imitation learning, particularly those arising in robotics, and the interesting theoretical and practical distinctions between it and more familiar frameworks like statistical supervised learning theory and reinforcement learning. Second, it provides roboticists and experts in applied artificial intelligence with a broader appreciation for the frameworks and tools available for imitation learning. It pays particular attention to the intimate connection between imitation learning approaches and those of structured prediction.
2. Design of Imitation Learning Algorithms
3. Behavioral Cloning
4. Inverse Reinforcement Learning
5. Challenges in Imitation Learning for Robotics