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Learning to grasp with parental scaffolding
(IEEE, 2011)
Parental scaffolding is an important mechanism utilized by infants during their development. Infants, for example, pay stronger attention to the features of objects highlighted by parents and learn the way of manipulating ...
Affordance-based altruistic robotic architecture for human–robot collaboration
(Sage, 2019-08)
This article proposes a computational model for altruistic behavior, shows its implementation on a physical robot, and presents the results of human-robot interaction experiments conducted with the implemented system. ...
Staged development of robot skills: behavior formation, affordance learning and imitation
(IEEE, 2015-06)
Inspired by infant development, we propose a three staged developmental framework for an anthropomorphic robot manipulator. In the first stage, the robot is initialized with a basic reach-and- enclose-on-contact movement ...
Parental scaffolding as a bootstrapping mechanism for learning grasp affordances and imitation skills
(Cambridge University Press, 2015-06)
Parental scaffolding is an important mechanism that speeds up infant sensorimotor development. Infants pay stronger attention to the features of the objects highlighted by parents, and their manipulation skills develop ...
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 ...
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 ...
Imitation and mirror systems in robots through Deep Modality Blending Networks
(Elsevier, 2022-02)
Learning to interact with the environment not only empowers the agent with manipulation capability but also generates information to facilitate building of action understanding and imitation capabilities. This seems to be ...
Deepsym: Deep symbol generation and rule learning for planning from unsupervised robot interaction
(AI Access Foundation, 2022)
Symbolic planning and reasoning are powerful tools for robots tackling complex tasks. However, the need to manually design the symbols restrict their applicability, especially for robots that are expected to act in open-ended ...
Bimanual rope manipulation skill synthesis through context dependent correction policy learning from human demonstration
(IEEE, 2023)
Learning from demonstration (LfD) with behavior cloning is attractive for its simplicity; however, compounding errors in long and complex skills can be a hindrance. Considering a target skill as a sequence of motor primitives ...
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