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Full-Text Articles in Physical Sciences and Mathematics
A Control Basis For Learning Multifingered Grasps, Jefferson Coelho, Roderic Grupen
A Control Basis For Learning Multifingered Grasps, Jefferson Coelho, Roderic Grupen
Roderic Grupen
In this paper, we introduce a robust controller that uses contact position and normal feedback to generate contact configurations for statically stable grasps. The approach uses a context sensitive composition of two controllers that minimize force and moment residuals in the grasp configuration. We show that equilibria in the composite controller correspond to optimal contact configurations for 2 and 3 contacts on regular, convex polygons. The preimage is used to generalize the controller to arbitrary object geometries by learning a policy for compensation and to address object recognition, and contact (de)allocation.
A Feedback Control Structure For On-Line Learning Tasks, Manfred Huber, Roderic Grupen
A Feedback Control Structure For On-Line Learning Tasks, Manfred Huber, Roderic Grupen
Roderic Grupen
This paper addresses adaptive control architectures for systems that respond autonomously to changing tasks. Such systems often have many sensory and motor alternatives and behavior drawn from these produces varying quality solutions. The objective is then to ground behavior in control laws which, combined with resources, enumerate closed-loop behavioral alternatives. Use of such controllers leads to analyzable and predictable composite system, permitting the construction of abstract behavioral models. Here, discrete event system and reinforcement learning techniques are employed to constrain the behavioral alternatives and to synthesize behavior on-line. To illustrate this, a quadruped
Learning To Coordinate Controllers -- Reinforcement Learning On A Control Basis, Manfred Huber, Roderic Grupen
Learning To Coordinate Controllers -- Reinforcement Learning On A Control Basis, Manfred Huber, Roderic Grupen
Roderic Grupen
Autonomous robot systems operating in an uncertain environment have to be reactive and adaptive in order to cope with changing environment conditions and task requirements. To achieve this, the hybrid control architecture presented in this paper uses reinforcement learning on top of a Discrete Event Dynamic System (DEDS) framework to learn to supervise a set of basis controllers in order to achieve a given task. The use of an abstract system model in the automatically derived supervisor reduces the complexity of the learning problem. In addition, safety constraints may be imposed a priori, such that the system learns on-line in …