Adaptation and Robust Learning of Probabilistic Movement Primitives
نویسندگان
چکیده
منابع مشابه
Probabilistic Movement Primitives
Movement Primitives (MP) are a well-established approach for representing modular and re-usable robot movement generators. Many state-of-the-art robot learning successes are based MPs, due to their compact representation of the inherently continuous and high dimensional robot movements. A major goal in robot learning is to combine multiple MPs as building blocks in a modular control architectur...
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This paper discusses a comprehensive framework for modular motor control based on a recently developed theory of dynamic movement primitives (DMP). DMPs are a formulation of movement primitives with autonomous nonlinear differential equations, whose time evolution creates smooth kinematic control policies. Model-based control theory is used to convert the outputs of these policies into motor co...
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Dynamic Movement Primitives have successfully been used to realize imitation learning, trial-and-error learning, reinforcement learning, movement recognition and segmentation and control. Because of this they have become a popular representation for motor primitives. In this work, we showcase how DMPs can be reformulated as a probabilistic linear dynamical system with control inputs. Through th...
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Robots that can learn over time by interacting with non-technical users must be capable of acquiring new motor skills, incrementally. The problem then is deciding when to teach the robot a new skill or when to rely on the robot generalizing its actions. This decision should be made by the robot and not by the human. Thus, the robot must quantify the suitability of its own skill given an unseen ...
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In this paper, we report on our research for learning biped locomotion from human demonstration. Our ultimate goal is to establish a design principle of a controller in order to achieve natural human-like locomotion. We suggest dynamical movement primitives as a CPG of a biped robot, an approach we have previously proposed for learning and encoding complex human movements. Demonstrated trajecto...
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ژورنال
عنوان ژورنال: IEEE Transactions on Robotics
سال: 2020
ISSN: 1552-3098,1941-0468
DOI: 10.1109/tro.2019.2937010