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A shared control method for online human-in-the-loop robot learning based on Locally Weighted Regression
(IEEE, 2016)
We propose a novel method that arbitrates the control between the human and the robot actors in a teaching-by-demonstration setting to form synergy between the two and facilitate effective skill synthesis on the robot. We ...
Modeling the development of infant imitation using inverse reinforcement learning
(IEEE, 2018-09)
Little is known about the computational mechanisms of how imitation skills develop along with infant sensorimotor learning. In robotics, there are several well developed frameworks for imitation learning or so called ...
Real-time decoding of arm kinematics during grasping based on F5 neural spike data
(Springer International Publishing, 2017)
Several studies have shown that the information related to grip type, object identity and kinematics of monkey grasping actions is available in macaque cortical areas of F5, MI, and AIP. In particular, these studies show ...
On the co-absence of input terms in higher order neuron representation of boolean functions
(Springer International Publishin, 2017)
Boolean functions (BFs) can be represented by using polynomial functions when −1 and +1 are used represent True and False respectively. The coefficients of the representing polynomial can be obtained by exact interpolation ...
Learning system dynamics via deep recurrent and conditional neural systems
(IEEE, 2021)
Although there are various mathematical methods for modeling system dynamics, more general solutions can be achieved using deep learning based on data. Alternative deep learning methods are presented in parallel with the ...
Trust me! I am a robot: an affective computational account of scaffolding in robot-robot interaction
(IEEE, 2021-08-08)
Forming trust in a biological or artificial interaction partner that provides reliable strategies and employing the learned strategies to scaffold another agent are critical problems that are often addressed separately in ...
Effect regulated projection of robot’s action space for production and prediction of manipulation primitives through learning progress and predictability based exploration
(IEEE, 2021-06)
In this study, we propose an effective action parameter exploration mechanism that enables efficient discovery of robot actions through interacting with objects in a simulated table-top environment. For this, the robot ...
Inferring cost functions using reward parameter search and policy gradient reinforcement learning
(IEEE, 2021)
This study focuses on inferring cost functions of obtained movement data using reward parameter search and policy gradient based Reinforcement Learning (RL). The behavior data for this task is obtained through a series of ...
Guest editorial special issue on continual unsupervised sensorimotor learning
(IEEE, 2021-06)
The pursuit of higher levels of autonomy and versatility in robotics is arguably led by two main factors. First, as we push robots out of the labs and production lines, it becomes increasingly challenging to design for all ...
Learning medical suturing primitives for autonomous suturing
(IEEE, 2021)
This paper focuses on a learning from demonstration approach for autonomous medical suturing. A conditional neural network is used to learn and generate suturing primitives trajectories which were conditioned on desired ...
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