|dc.description.abstract||High fidelity, compliant robot control requires a sufficiently accurate dynamics
model. Often though, it is not possible to obtain a dynamics model sufficiently accurately
or at all using analytical methods. In such cases, an alternative is to learn the
dynamics model from movement data. This thesis discusses the problems specific to
dynamics learning for control under nonstationarity of the dynamics.
We refer to the cause of the nonstationarity as the context of the dynamics. Contexts
are, typically, not directly observable. For instance, the dynamics of a robot manipulator
changes as the robot manipulates different objects and the physical properties of
the load – the context of the dynamics – are not directly known by the controller. Other
examples of contexts that affect the dynamics are changing force fields or liquids with
different viscosity in which a manipulator has to operate.
The learned dynamics model needs to be adapted whenever the context and therefore
the dynamics changes. Inevitably, performance drops during the period of adaptation.
The goal of this work, is to reuse and generalize the experience obtained by
learning the dynamics of different contexts in order to adapt to changing contexts fast.
We first examine the case that the dynamics may switch between a discrete, finite
set of contexts and use multiple models and switching between them to adapt the
controller fast. A probabilistic formulation of multiple models is used, where a discrete
latent variable is used to represent the unobserved context and index the models.
In comparison to previous multiple model approaches, the developed method is able
to learn multiple models of nonlinear dynamics, using an appropriately modified EM
We also deal with the case when there exists a continuum of possible contexts that
affect the dynamics and hence, it becomes essential to generalize from a set of experienced
contexts to novel contexts. There is very little previous work on this direction
and the developed methods are completely novel. We introduce a set of continuous
latent variables to represent context and introduce a dynamics model that depends on
this set of variables. We first examine learning and inference in such a model when
there is strong prior knowledge on the relationship of these continuous latent variables
to the modulation of the dynamics, e.g., when the load at the end effector changes. We
also develop methods for the case that there is no such knowledge available.
Finally, we formulate a dynamics model whose input is augmented with observed
variables that convey contextual information indirectly, e.g., the information from tactile
sensors at the interface between the load and the arm. This approach also allows
generalization to not previously seen contexts and is applicable when the nature of the
context is not known. In addition, we show that use of such a model is possible even
when special sensory input is not available by using an instance of an autoregressive
The developed methods are tested on realistic, full physics simulations of robot
arm systems including a simplistic 3 degree of freedom (DOF) arm and a simulation
of the 7 DOF DLR light weight robot arm. In the experiments, varying contexts are
different manipulated objects. Nevertheless, the developed methods (with the exception
of the methods that require prior knowledge on the relationship of the context to
the modulation of the dynamics) are more generally applicable and could be used to
deal with different context variation scenarios.||en