A Probabilistic Representation for Dynamic Movement Primitives

TL;DR

This paper reformulates Dynamic Movement Primitives as a probabilistic linear dynamical system, enabling inference-based control, failure detection, and integration with Kalman filtering for improved robotic movement execution.

Contribution

It introduces a probabilistic reformulation of DMPs allowing for inference, feedback modulation, and failure detection during movement execution.

Findings

Probabilistic DMPs enable Kalman filtering for inference.

Inference provides automatic feedback control.

Initial results show successful failure detection.

Abstract

Dynamic Movement Primitives have successfully been used to realize imitation learning, trial-and-error learning, reinforce- ment learning, movement recognition and segmentation and control. Because of this they have become a popular represen- tation for motor primitives. In this work, we showcase how DMPs can be reformulated as a probabilistic linear dynamical system with control inputs. Through this probabilistic repre- sentation of DMPs, algorithms such as Kalman filtering and smoothing are directly applicable to perform inference on pro- prioceptive sensor measurements during execution. We show that inference in this probabilistic model automatically leads to a feedback term to online modulate the execution of a DMP. Furthermore, we show how inference allows us to measure the likelihood that we are successfully executing a given motion primitive. In this context, we show initial results of using the probabilistic model to detect execution failures on a simulated movement primitive dataset.