Article

Autonomous Robots

, Volume 27, Issue 2, pp 105-121

First online:

A novel method for learning policies from variable constraint data

  • Matthew HowardAffiliated withInstitute of Perception Action and Behaviour, University of Edinburgh Email author 
  • , Stefan KlankeAffiliated withInstitute of Perception Action and Behaviour, University of Edinburgh
  • , Michael GiengerAffiliated withHonda Research Institute Europe (GmBH)
  • , Christian GoerickAffiliated withHonda Research Institute Europe (GmBH)
  • , Sethu VijayakumarAffiliated withInstitute of Perception Action and Behaviour, University of Edinburgh

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Abstract

Many everyday human skills can be framed in terms of performing some task subject to constraints imposed by the environment. Constraints are usually unobservable and frequently change between contexts. In this paper, we present a novel approach for learning (unconstrained) control policies from movement data, where observations come from movements under different constraints. As a key ingredient, we introduce a small but highly effective modification to the standard risk functional, allowing us to make a meaningful comparison between the estimated policy and constrained observations. We demonstrate our approach on systems of varying complexity, including kinematic data from the ASIMO humanoid robot with 27 degrees of freedom, and present results for learning from human demonstration.

Keywords

Direct policy learning Constrained motion Imitation Nullspace control