Incremental local online Gaussian Mixture Regression for imitation learning of multiple tasks

By Thomas Cederborg, Ming Li, Adrien Baranes, Pierre-Yves Oudeyer. 2010

92 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering

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Citations in the corpus

An Incremental Self-Organizing Architecture for Sensorimotor Learning and Prediction (2017, methodology): “[38] suggested to perform a local partitioning of the input space through kd-trees and training several local GMR models.”
A Bio-Inspired Incremental Learning Architecture for Applied Perceptual Problems (2016, background): “Equally, the choice of local models varies between linear models [5], Gaussian mixture regression [9] or Gaussian processes [6].”
Bootstrapping Intrinsically Motivated Learning with Human Demonstrations (unknown): “Studies in robot learning by imitation and demonstration have already developed statistical inference mechanisms allowing the inference of new task constraints [4], [5], [7].”
Gradient-based training of Gaussian Mixture Models in High-Dimensional Spaces (2019, background): “There are several proposals for what is termed “incremental” or “online” EM for training GMMs Vlassis & Likas (2002); Engel & Heinen (2010); Pinto & Engel (2015); Cederborg et al. (2010); Song & Wang (2005); Kristan et al. (2008), allowing GMMs to be trained by providing one sample at a time.”
Intrinsically Motivated Learning of Real-World Sensorimotor Skills with Developmental Constraints (2013, methodology): “…imitation/demonstration has elaborated many algorithms allowing the acquisition of new motor primitives (Billard et al., 2008; Calinon et al., 2007; Cederborg et al., 2010; Grollman and Jenkins, 2010), and future research in developmental robotics might strongly benefit from integrating…”
Towards transferring skills to flexible surgical robots with programming by demonstration and reinforcement learning (2016, background): “Therefore, in this work, GMM is used to model the human demonstrations and the motor movements are generalized by GMR.”
Socially guided intrinsic motivation for robot learning of motor skills (2013, background): “…to model the invariances of demonstrated movements (Billard et al 2007; Grollman and Jenkins 2008; Chernova and Veloso 2009; Lopes et al. 2009b; Cederborg et al. 2010; Calinon 2009; Calinon and Billard 2007; Peters and Schaal 2008), or inverse reinforcement learning approaches (Abbeel and Ng…”
On-line regression algorithms for learning mechanical models of robots: A survey (2011, methodology): “Several papers present a performance comparison between some of the systems reviewed here [21, 39, 72, 74].”
A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms (2019, methodology): “Behavioral cloning is often used as a stand-alone learning method [316, 19, 390, 288, 55, 6, 96, 151, 327], as well as a way to provide a better starting point for reinforcement learning [289], though this additionally requires the specification of a reward function for RL to optimize.”
Bootstrapping intrinsically motivated learning with human demonstration (2011, background): “Studies in robot learning by imitation and demonstration have already developed statistical inference mechanisms allowing the inference of new task constraints [4], [5], [7].”