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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. ...
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 ...
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 ...
Trust in robot–robot scaffolding
(IEEE, 2023-12-01)
The study of robot trust in humans and other agents is not explored widely despite its importance for the near future human-robot symbiotic societies. Here, we propose that robots should trust partners that tend to reduce ...
ACNMP: skill transfer and task extrapolation through learning from demonstration and reinforcement learning via representation sharing
(ML Research Press, 2020)
To equip robots with dexterous skills, an effective approach is to first transfer the desired skill via Learning from Demonstration (LfD), then let the robot improve it by self-exploration via Reinforcement Learning (RL). ...
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