English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Probability distributions are key components of many learning fromdemonstration (LfD) approaches. While the configuration of a manipulator isdefined by its joint angles, poses are often best explained within several taskspaces. In many approaches, distributions within relevant task spaces arelearned independently and only combined at the control level. Thissimplification implies several problems that are addressed in this work. Weshow that the fusion of models in different task spaces can be expressed as aproduct of experts (PoE), where the probabilities of the models are multipliedand renormalized so that it becomes a proper distribution of joint angles.Multiple experiments are presented to show that learning the different modelsjointly in the PoE framework significantly improves the quality of the model.The proposed approach particularly stands out when the robot has to learncompetitive or hierarchical objectives. Training the model jointly usuallyrelies on contrastive divergence, which requires costly approximations that canaffect performance. We propose an alternative strategy using variationalinference and mixture model approximations. In particular, we show that theproposed approach can be extended to PoE with a nullspace structure (PoENS),where the model is able to recover tasks that are masked by the resolution ofhigher-level objectives.

Learning from demonstration using products of experts: applications to manipulation and task prioritization