Learning Generalizable Control Programs
By Stephen Hart, R. Grupen. 2011
65 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Cognitive Neuroscience, Control and Systems Engineering
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Sergey Levine
- Jan Peters
- Pieter Abbeel
- Gerhard Neumann
- George Dimitri Konidaris
- J. Bagnell
- Chelsea Finn
- Jens Kober
In Cognitive Neuroscience
- Gianluca Baldassarre
- Jan Peters
- Gerhard Neumann
- Marco Mirolli
- Francesco Mannella
- George Dimitri Konidaris
- Christian Daniel
- J. Bagnell
In Control and Systems Engineering
- Jan Peters
- Gerhard Neumann
- J. Bagnell
- Jens Kober
- Sergey Levine
- Oliver Kroemer
- George Dimitri Konidaris
- Christian Daniel
Citations in the corpus
- Reinforcement learning in robotics: A survey (2013, methodology): “…and Wang et al. (2006) Prior knowledge through task structuring Approach Employed by… Hierarchical Daniel et al. (2012), Donnart and Meyer (1996), Hart and Grupen (2011), Huber and Grupen (1997), Kirchner (1997), Morimoto and Doya (2001), Muelling et al. (2012), and Whitman and Atkeson (2010)…”
- Towards learning hierarchical skills for multi-phase manipulation tasks (2015, background): “Hiearchical reinforcement learning methods learn both a library of skills as well as a high-level policy for sequencing them [37], [13], [20], [34], [15].”
- Intrinsically motivated action-outcome learning and goal-based action recall: a system-level bio-constrained computational model. (2013, background): “…is quite common in the computational literature, since the classic models on IM (Schmidhuber, 1991b,a; Oudeyer et al., 2007) (but there are important models that use CB-IM mechanisms to acquire competence, e.g., Singh S. and Chentanez, 2005; Hart and Grupen, 2011; Schembri et al., 2007c).”
- Intrinsically Motivated Learning in Natural and Artificial Systems (2013, background): “The last contribution of this part is by Stephen Hart and Roderic Grupen, two roboticists who have proposed some of the most advanced robotic applications based on intrinsic motivations (Hart and Grupen 2011).”
- Online Learning with Stochastic Recurrent Neural Networks using Intrinsic Motivation Signals (2017, background): “, [8, 9, 10, 11, 12, 13], only few approaches so far go beyond a reinforcement learning setup.”
- Autonomous Skill Acquisition on a Mobile Manipulator (2011, background): “…outside of the context of any particular task, by developing agents that are intrinsically motivated to acquire skills that may become useful in the future, as opposed to extrinsically motivated by a particular task (Singh, Barto, and Chentanez 2005; Oudeyer and Kaplan 2007; Hart and Grupen 2011).”