Motion learning from observation using Affinity Propagation clustering
Guoting Chang, Dana Kulić · 2013
During robot imitation learning, a key problem when observing the motions of a demonstrator is the modeling and recognition of movement prototypes. This paper proposes using Affinity Propagation (AP) to cluster motions modeled using either Dynamic Movement Primitives (DMPs) or Hidden Markov Models (HMMs). The proposed AP clustering algorithm is simple and efficient, provides robust results and automatically identifies representative exemplars for each motion group, leading to a minimal representation of the observations that can also be used to generate motions. In experiments using videos and motion capture data of human demonstrations, it is shown that the weight parameters of the DMP model can be used as features for motion recognition and the proposed method can distinguish between different (coarse distinction) or similar (fine distinction) motion groups.