Special Issue on the 2021 Ubiquitous Robots Conference

Intelligent Service Robotics -     

Learning task-parameterized dynamic movement primitives using mixture of GMMs

Task-parameterized skill learning aims at adaptive motion encoding to new situations. While existing approaches for task-parameterized skill learning have demonstrated good adaptation within the demonstrated region, the extrapolation problem of task-parameterized skills has not been investigated enough. In this work, with the aim of good adaptation not only within the demonstrated region but also outside of the region, we propose to combine a generative model with a dynamic movement primitive by formulating learning as a density estimation problem. Moreover, for efficient learning from relatively few demonstrations, we propose to augment training data with additional incomplete data. The proposed method is tested and compared with existing works in simulations and real robot experiments. Experimental results verified its generalization in the extrapolation region.     

Incremental kinesthetic teaching of motion primitives using the motion refinement tube

We present an approach for kinesthetic teaching of motion primitives for a humanoid robot. The proposed teaching method starts with observational learning and applies iterative kinesthetic motion refinement using a forgetting factor. Kinesthetic teaching is supported by introducing the motion refinement tube, which represents an area of allowed motion refinement around the nominal trajectory. On the realtime control level, the kinesthetic teaching is handled by a customized impedance controller, which combines tracking performance with compliant physical interaction and allows to implement soft boundaries for the motion refinement. A novel method for continuous generation of motions from a hidden Markov model (HMM) representation of motion primitives is proposed, which incorporates time information for each state. The proposed methods were implemented and tested using DLR??s humanoid upper-body robot Justin.