2004 · 122 citations · 6 references
EngineeringNeuroscienceRobotics EngineeringGraspingRobot ControlRoboticsCollaborative ManipulationGrasp DetectionGrasp PlanningLearning From DemonstrationManipulation ControlRobot ManipulationGraspGrasp Representation LearningInternal Models ApproachLearning-based Grasp SynthesisLearning-based GraspingManipulationRobot GraspingRobotic Manipulation Learning
Human control strategies rely on comparing sensory feedback with internal models, and recent neuroscience shows that predictions reduce sensory delays and enable more complex tasks. The paper aims to evaluate, recognize, and model human grasps during arm transport and to learn grasp strategies for different robotic hands. The authors develop an artificial cognitive system that performs complex manipulation tasks by evaluating, recognizing, and modeling human grasps during arm transport and learning grasp strategies for various robotic hands.
We describe our effort in development of an artificial cognitive system, able of performing complex manipulation tasks in a teleoperated or collaborative manner. Some of the work is motivated by human control strategies that, in general, involve comparison between sensory feedback and a-priori known, internal models. According to recent neuroscientific findings, predictions help to reduce the delays in obtaining the sensory information and to perform more complex tasks. This paper deals with the issue of robotic manipulation and grasping in particular. Two main contributions of the paper are: i) evaluation, recognition and modeling of human grasps during the arm transportation sequence, and ii) learning and representation of grasp strategies for different robotic hands.
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The Robonaut hand: a dexterous robot hand for space
Chris S. Lovchik, Myron Diftler · 2003 · 537 citations
Engineering, Aerospace Engineering, Robotics Engineering +17
Systems integration for real-world manipulation tasks
Lars Petersson, Patric Jensfelt, Dennis Tell et al. · 2003 · 45 citations