6 accepted papers
Trajectory segmentation refers to dividing a trajectory into meaningful consecutive sub-trajectories. This paper focuses on trajectory segmentation for 3D rigid-body motions. Most segmentation approaches in the literature represent the body’s trajectory as a point trajectory, considering only its tr…
Learning from Demonstration (LfD) requires methodologies able to generalize tasks in new situations. This paper studies the use of virtual demonstrations to extend the extrapolation capabilities of probabilistic motion models such as the traPPCA method. Similarly to other LfD methods, traPPCA is abl…
Variation in task requirements and operation in uncertain environments require robots that can continuously adapt their motions during execution. To do this successfully, the original motion characteristics must be preserved. This paper presents an approach for on-line adaptation of end-effector tra
Invariant representations of demonstrated motion trajectories provide context-independent motion models that can be used in motion recognition and generalization applications such as robot programming by demonstration. In practice, the use of invariant representations is still limited because their…
In programming by demonstration, generalization is necessary to apply demonstrated motions in novel situations. Many existing trajectory representations have poor generalization capabilities since they are built on trajectory coordinates that depend on the context in which the motion is recorded. In…
This paper presents an overview and comparison of minimal and complete rigid body motion trajectory descriptors, usable in applications like motion recognition and programming by demonstration. Motion trajectory descriptors are able to deal with potentially unwanted variations acting on the motion t…