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Interaction primitives for human-robot cooperation tasks
TLDR
This paper proposes to learn interaction skills by observing how two humans engage in a similar task, and introduces a new representation called Interaction Primitives, which builds on the framework of dynamic motor primitives by maintaining a distribution over the parameters of the DMP. Expand
Probabilistic movement modeling for intention inference in human–robot interaction
TLDR
The Intention-Driven Dynamics Model is proposed to probabilistically model the generative process of movements that are directed by the intention and allows the intention to be inferred from observed movements using Bayes’ theorem. Expand
Physical Human-Robot Interaction: Mutual Learning and Adaptation
TLDR
It is shown that this algorithm helps to improve the quality of the interaction between a robot and a human caregiver and two human-in-the-loop learning scenarios that are inspired by human parenting behavior are presented. Expand
Learning multiple collaborative tasks with a mixture of Interaction Primitives
TLDR
A Mixture of Interaction Primitives is proposed to learn multiple interaction patterns from unlabeled demonstrations to overcome the limitation of this framework to represent and generalize a single interaction pattern. Expand
Learning interaction for collaborative tasks with probabilistic movement primitives
TLDR
This paper introduces the use of Probabilistic Movement Primitives (ProMPs) to devise an interaction method that both recognizes the action of a human and generates the appropriate movement primitive of the robot assistant. Expand
Intelligent exploration for genetic algorithms: using self-organizing maps in evolutionary computation
TLDR
The evaluation of GASOM on well known problems shows that it effectively prevents premature convergence and seeks the global optimum. Expand
Generalization of human grasping for multi-fingered robot hands
TLDR
An imitation learning approach for learning and generalizing grasping skills based on human demonstrations is presented, which learns low-dimensional latent grasp spaces for different grasp types which form the basis for a novel extension to dynamic motor primitives. Expand
Grasp Recognition with Uncalibrated Data Gloves - A Comparison of Classification Methods
TLDR
It is shown that a reasonably well to highly reliable recognition of grasp types can be achieved - depending on whether or not the glove user is among those training the classifier - even with uncalibrated data gloves, and the best performing classification methods are identified. Expand
A Human–Robot Interaction Perspective on Assistive and Rehabilitation Robotics
TLDR
A human–robot interaction perspective on current issues and opportunities in the field of assisted and rehabilitation devices is given and options to provide sensory user feedback that are currently missing from robotic devices are outlined. Expand
Probabilistic Modeling of Human Movements for Intention Inference
TLDR
The Intention-Driven Dynamics Model (IDDM), a latent variable model for inferring unknown human intentions, is proposed and an efficient approximate inference algorithm to infer the human’s intention from an ongoing movement is introduced. Expand
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