Learning Sensor Feedback Models from Demonstrations via Phase-Modulated Neural Networks

Abstract

In order to robustly execute a task under environmental uncertainty, a robot needs to be able to reactively adapt to changes arising in its environment. The environment changes are usually reflected in deviation from expected sensory traces. These deviations in sensory traces can be used to drive the motion adaptation, and for this purpose, a feedback model is required. The feedback model maps the deviations in sensory traces to the motion plan adaptation. In this paper, we develop a general data-driven framework for learning a feedback model from demonstrations. We utilize a variant of a radial basis function network structure --with movement phases as kernel centers-- which can generally be applied to represent any feedback models for movement primitives. To demonstrate the effectiveness of our framework, we test it on the task of scraping on a tilt board. In this task, we are learning a reactive policy in the form of orientation adaptation, based on deviations of tactile sensor traces. As a proof of concept of our method, we provide evaluations on an anthropomorphic robot. A video demonstrating our approach and its results can be seen in https://youtu.be/7Dx5imy1Kcw

8 pages, accepted to be published at the International Conference on Robotics and Automation (ICRA) 2018

References in corpus (1)

• Learning Feedback Terms for Reactive Planning and Control

Cited by in corpus (5)

• Dynamic Movement Primitives in Robotics: A Tutorial Survey
• Dynamic Movement Primitives: Volumetric Obstacle Avoidance Using Dynamic Potential Functions
• Residual Learning from Demonstration: Adapting DMPs for Contact-rich Manipulation
• Learning Latent Space Dynamics for Tactile Servoing
• Supervised Learning and Reinforcement Learning of Feedback Models for Reactive Behaviors: Tactile Feedback Testbed