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Reinforcement learning to adjust parametrized motor primitives to new situations
(Springer Science+Business Media, 2012-11)
Humans manage to adapt learned movements very quickly to new situations by generalizing learned behaviors from similar situations. In contrast, robots currently often need to re-learn the complete movement. In this paper, ...
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 ...
Symbol emergence in cognitive developmental systems: A survey
(IEEE, 2019-12)
Humans use signs, e.g., sentences in a spoken language, for communication and thought. Hence, symbol systems like language are crucial for our communication with other agents and adaptation to our real-world environment. ...
Environmental force estimation for a robotic hand : compliant contact detection
(IEEE, 2015)
This paper presents a model based compensation method to enable environmental force estimation for a robotic hand with no tactile or force sensors. To this end, we utilize multi-joint robot dynamics and disturbance observer ...
Emotion as an emergent phenomenon of the neurocomputational energy regulation mechanism of a cognitive agent in a decision-making task
(Sage, 2021-02)
Biological agents need to complete perception-action cycles to perform various cognitive and biological tasks such as maximizing their wellbeing and their chances of genetic continuation. However, the processes performed ...
Force reference extraction via human interaction for a robotic polishing task: Force-induced motion
In this paper, a method to control a manipulator using force-induced trajectory is proposed. The trajectory is learned from an operator doing the polishing task using a tool attached to the robot's end-effector. The learning ...
Human-in-the-loop control and task learning for pneumatically actuated muscle based robots
(Frontiers Media, 2018-11-06)
Pneumatically actuated muscles (PAMs) provide a low cost, lightweight, and high power-To-weight ratio solution for many robotic applications. In addition, the antagonist pair configuration for robotic arms make it open to ...
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 ...
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