Body schema acquisition through active learning

By Ruben Martinez-Cantin, Manuel Lopes, Luis Montesano. 2010

52 citations recorded by the source corpus.

We present an active learning algorithm for the problem of body schema learning, i.e. estimating a kinematic model of a serial robot. The learning process is done online using Recursive Least Squares (RLS) estimation, which outperforms gradient methods usually applied in the literature. In addiction, the method provides the required information to apply an active learning algorithm to find the optimal set of robot configurations and observations to improve the learning process. By selecting the most informative observations, the proposed method minimizes the required amount of data. We have developed an efficient version of the active learning algorithm to select the points in real-time. The algorithms have been tested and compared using both simulated environments and a real humanoid robot.

Subfields: Biomedical Engineering, Control and Systems Engineering

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Citations in the corpus

Active learning of inverse models with intrinsically motivated goal exploration in robots (2013, neutral): “real robots. Nevertheless examples that consider robotic problems already exist for a large variety of problems: building environment maps [67, 118], reinforcement learning [12], body schema learning [25], imitation [61, 27], exploration of objects and body properties [77], manipulation [46], among ”
Online Goal Babbling for rapid bootstrapping of inverse models in high dimensions (2011, background): “The exploration can be significantly improved by active learning schemes [4], [5], frequently discussed under the notion of intrinsic motivation [6].”
On-line regression algorithms for learning mechanical models of robots: A survey (2011, background): “Jagersand [47]) and more general work on the acquisition of a body schema or a body image that directly relate visual features to mechanical properties of the plant [43, 62, 64, 99].”
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.”
Model learning for robot control: a survey (2011, methodology): “Inspired by the biologically motivated body representations, the concept of body schema has been exploited in robotics, such as for data generation (Martinez-Cantin et al. 2010) or incorporating structural prior knowledge (Ulbrich et al.”
Autonomous Online Learning of Reaching Behavior in a humanoid Robot (2012, background): “Learning kinematic models for reaching is a particular case of learning the body schema, a problem which has been addressed in recent works.(14,15,18) In Refs.”
Bootstrapping bilinear models of Simple Vehicles (2015, methodology): “D(Y;X;U; ∆) is the set of all discrete-time dynamical systems with sampling interval ∆.”