Learning compact parameterized skills with a single regression

By Freek Stulp, Gennaro Raiola, Antoine Hoarau, Serena Ivaldi, Olivier Sigaud. 2013

90 citations recorded by the source corpus.

One of the long-term challenges of programming by demonstration is achieving generality, i.e. automatically adapting the reproduced behavior to novel situations. A common approach for achieving generality is to learn parameterizable skills from multiple demonstrations for different situations. In this paper, we generalize recent approaches on learning parameterizable skills based on dynamical movement primitives (DMPs), such that task parameters are also passed as inputs to the function approximator of the DMP. This leads to a more general, flexible, and compact representation of parameterizable skills, as demonstrated by our empirical evaluation on the iCub and Meka humanoid robots.

Subfields: Artificial Intelligence, Control and Systems Engineering, Mechanical Engineering, Reinforcement Learning

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Citations in the corpus

Learning Parameterized Skills (2012, background): “[69] proposed further extensions to the DMP framework by allowing additional tunable parameters to be learned; they focus their attention, however, to the learning-from-demonstration setting.”
One-Shot Visual Imitation Learning via Meta-Learning (2017, methodology): “Unlike prior methods that take the task identity [1, 2, 3, 4] or a demonstration [5] as the input into a contextual policy, our approach learns a parameterized policy that can be adapted to different tasks through gradient updates, effectively learning to imitation learn.”
Preparing for the Unknown: Learning a Universal Policy with Online System Identification (2017, methodology): “Many existing methods [7, 33] employ an ensemble approach by learning a discrete set of control policies and consolidating them into one regression model.”
Learning control Lyapunov function to ensure stability of dynamical system-based robot reaching motions (2014, methodology): “During the last decade, DS has been used to model discrete motions [6, 7, 29, 30], rhythmic motions [31, 32], hitting motions [33, 34, 7], obstacle avoidance [35, 36, 37], etc.”
Tensor Based Knowledge Transfer Across Skill Categories for Robot Control (2017, background): “For example throwing objects of different weights [ Stulp et al. , 2014 ] , throwing to different target locations [ Kupcsik et al. , 2013 ] , or going via different waypoints to avoid various obstacles [ Stulp et al. , 2013 ] .”
Learning Deep Parameterized Skills from Demonstration for Re-targetable Visuomotor Control (2019, methodology): “Parameterized skill learning as described by da Silva et al. [4] aims to learn a mapping from a given task parameter vector to a policy [4, 5, 6, 9, 16, 17, 21, 22, 24].”