PIPE: Process Informed Parameter Estimation, a learning based approach to task generalized system identification

TL;DR

This paper introduces PIPE, a learning-based system that estimates contact models for robot assembly tasks using geometric features and physical knowledge, enabling generalization to new parts with minimal data.

Contribution

The paper proposes a novel neural network structure that estimates contact models based on part geometry, reducing the need for new data for each assembly task.

Findings

Accurately estimated contact models for unknown objects.

Achieved good results with limited training data.

Validated on real robot assembly experiments.

Abstract

We address the problem of robot guided assembly tasks, by using a learning-based approach to identify contact model parameters for known and novel parts. First, a Variational Autoencoder (VAE) is used to extract geometric features of assembly parts. Then, we combine the extracted features with physical knowledge to derive the parameters of a contact model using our newly proposed neural network structure. The measured force from real experiments is used to supervise the predicted forces, thus avoiding the need for ground truth model parameters. Although trained only on a small set of assembly parts, good contact model estimation for unknown objects were achieved. Our main contribution is the network structure that allows us to estimate contact models of assembly tasks depending on the geometry of the part to be joined. Where current system identification processes have to record new data for a new assembly process, our method only requires the 3D model of the assembly part. We evaluate our method by estimating contact models for robot-guided assembly tasks of pin connectors as well as electronic plugs and compare the results with real experiments.