# Learning to combine primitive skills: A step towards versatile robotic manipulation

## TL;DR

This paper introduces a reinforcement learning approach for robotic manipulation that learns to combine primitive skills using visual inputs, without requiring intermediate rewards or full demonstrations, and successfully transfers from simulation to real robots.

## Contribution

The method enables versatile, vision-based task planning that handles occlusions and scene changes, with efficient training from synthetic data and successful real-world transfer.

## Key findings

- High success rates in real robot manipulation tasks
- Effective training from few synthetic demonstrations
- Robust performance under occlusions and dynamic scenes

## Abstract

Manipulation tasks such as preparing a meal or assembling furniture remain highly challenging for robotics and vision. Traditional task and motion planning (TAMP) methods can solve complex tasks but require full state observability and are not adapted to dynamic scene changes. Recent learning methods can operate directly on visual inputs but typically require many demonstrations and/or task-specific reward engineering. In this work we aim to overcome previous limitations and propose a reinforcement learning (RL) approach to task planning that learns to combine primitive skills. First, compared to previous learning methods, our approach requires neither intermediate rewards nor complete task demonstrations during training. Second, we demonstrate the versatility of our vision-based task planning in challenging settings with temporary occlusions and dynamic scene changes. Third, we propose an efficient training of basic skills from few synthetic demonstrations by exploring recent CNN architectures and data augmentation. Notably, while all of our policies are learned on visual inputs in simulated environments, we demonstrate the successful transfer and high success rates when applying such policies to manipulation tasks on a real UR5 robotic arm.