Multi-Task Learning with Sequence-Conditioned Transporter Networks

TL;DR

This paper introduces a new benchmark and a vision-based system for multi-task robotic manipulation, demonstrating improved performance on complex, compositional tasks through sequence-conditioning and weighted sampling techniques.

Contribution

The work presents a novel benchmark suite and an end-to-end architecture that enhances multi-task learning efficiency and generalization in robotic manipulation tasks.

Findings

Significant performance improvements on the MultiRavens benchmark.

Effective multi-task learning with sequence-conditioning and weighted sampling.

Enhanced learning efficiency for long-horizon manipulation tasks.

Abstract

Enabling robots to solve multiple manipulation tasks has a wide range of industrial applications. While learning-based approaches enjoy flexibility and generalizability, scaling these approaches to solve such compositional tasks remains a challenge. In this work, we aim to solve multi-task learning through the lens of sequence-conditioning and weighted sampling. First, we propose a new suite of benchmark specifically aimed at compositional tasks, MultiRavens, which allows defining custom task combinations through task modules that are inspired by industrial tasks and exemplify the difficulties in vision-based learning and planning methods. Second, we propose a vision-based end-to-end system architecture, Sequence-Conditioned Transporter Networks, which augments Goal-Conditioned Transporter Networks with sequence-conditioning and weighted sampling and can efficiently learn to solve multi-task long horizon problems. Our analysis suggests that not only the new framework significantly improves pick-and-place performance on novel 10 multi-task benchmark problems, but also the multi-task learning with weighted sampling can vastly improve learning and agent performances on individual tasks.