Intrinsically motivated hierarchical manipulation

By Stephen Hart, Shiraj Sen, R. Grupen. 2008

39 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Biomedical Engineering, Control and Systems Engineering

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Citations in the corpus

Formal Theory of Creativity, Fun, and Intrinsic Motivation (1990–2010) (2010, background): “Other researchers in the nascent field of developmental robotics [9], [20], [26], [27], [38], [51], [52], [57], [64], [113] and intrinsic reward also took up the basic idea, for example, Oudeyer et al.”
Guest Editorial Active Learning and Intrinsically Motivated Exploration in Robots: Advances and Challenges (2010, background): “Nevertheless, examples that consider robotic problems already exist for a large variety of problems: mapping [21], [22], reinforcement learning [23], body schema learning [25], imitation [26], [27], exploration of objects and body properties [7], manipulation [28], among many others.”
Competence progress intrinsic motivation (2010, background): “There is more work considering the creation of skills in addition to models, employing other approaches to intrinsic motivation, including [1] and [18].”
Intrinsically Motivated Learning of Real-World Sensorimotor Skills with Developmental Constraints (2013, background): “…in some of the most advanced real-world intrinsically motivated robot experiments so far, such as in the Playground Experiment (Oudeyer and Kaplan, 2006; Oudeyer et al., 2007) or in (Hart and Grupen, 2008) where primitives were based on sophisticated control-theoretic sensorimotor feedback loops.”
Learning Generalizable Control Programs (2011, methodology): “We now define an intrinsic motivation function for affordance discovery, adapted from [10].”
Active Learning and Intrinsically Motivated Exploration in Robots : Advances and Challenges (2010, background): “Nevertheless, examples that consider robotic problems already exist for a large variety of problems: mapping [21], [22], reinforcement learning [23], body schema learning [25], imitation [26], [27], exploration of objects and body properties [7], manipulation [28], among many others.”
A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms (2019, methodology): “This is commonly done using demonstration trajectories, though it could also be performed on trajectories generated autonomously by the robot, typically after learning [143, 202, 312].”