Learning concurrent motor skills in versatile solution spaces

By Christian Daniel, Gerhard Neumann, Jan Peters. 2012

44 citations recorded by the source corpus.

Future robots need to autonomously acquire motor skills in order to reduce their reliance on human programming. Many motor skill learning methods concentrate on learning a single solution for a given task. However, discarding information about additional solutions during learning unnecessarily limits autonomy. Such favoring of single solutions often requires re-learning of motor skills when the task, the environment or the robot's body changes in a way that renders the learned solution infeasible. Future robots need to be able to adapt to such changes and, ideally, have a large repertoire of movements to cope with such problems. In contrast to current methods, our approach simultaneously learns multiple distinct solutions for the same task, such that a partial degeneration of this solution space does not prevent the successful completion of the task. In this paper, we present a complete framework that is capable of learning different solution strategies for a real robot Tetherball task.

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

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Citations in the corpus

Reinforcement learning in robotics: A survey (2013, methodology): “Prior knowledge through demonstration Approach Employed by… Teacher Atkeson and Schaal (1997), Bentivegna et al. (2004), Bitzer et al. (2010), Conn and Peters II (2007), Gräve et al. (2010), Kober et al. (2008); Kober and Peters (2009), Latzke et al. (2007), and Peters and Schaal (2008c,b) Policy Bi”
A Survey on Policy Search for Robotics (2013, methodology): “These experiments include Baseball, Ball-InThe-Cup, Dart-Throwing, Pan-Cake Flipping and Tetherball.”
Robobarista: Object Part Based Transfer of Manipulation Trajectories from Crowd-Sourcing in 3D Pointclouds (2015, background): “on high level task composition [32, 10] or is often limited to previously seen objects [38, 37].”
Probabilistic Movement Primitives (2013, methodology): “MPs have been used to solve many complex tasks, including ‘Ball-in-the-Cup’ [4], Ball-Throwing [5, 6], Pancake-Flipping [7] and Tetherball [8].”
Information-Theoretic Motor Skill Learning (2013, methodology): “Our algorithm is based on the Relative Entropy Policy Search Algorithm (REPS) proposed by Peters et. al.”