# Socially Guided Intrinsic Motivation for Robot Learning of Motor Skills

arXiv: https://arxiv.org/abs/1804.07269 · HTML: https://mlsanity.com/paper/1804.07269/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Robot manipulation imitation learning, one of 13 directions in Offline RL and imitation learning from data.
- First posted on arXiv 19 Apr 2018.

## Placed in

- Offline RL and imitation learning from data / [Robot manipulation imitation learning](https://mlsanity.com/offlinerl/robot-manipulation-imitation-learning/)

## Built on by (most cited first, 5 of 5)

- [Open-Ended Goal Inference through Actions and Language for Human-Robot Collaboration](https://mlsanity.com/paper/2512.04453/) (2025)
- [Intrinsically Motivated Open-Ended Multi-Task Learning Using Transfer Learning to Discover Task Hierarchy](https://mlsanity.com/paper/2102.09854/) (2021)
- [Help Me Explore: Minimal Social Interventions for Graph-Based Autotelic Agents](https://mlsanity.com/paper/2202.05129/) (2022)
- [Robots Learn Increasingly Complex Tasks with Intrinsic Motivation and Automatic Curriculum Learning](https://mlsanity.com/paper/2202.10222/) (2022)
- [HEART: Coordination of Heterogeneous Expert Agents for Physically Grounded Robotic Task Planning](https://mlsanity.com/paper/2606.25404/) (2026)

## Abstract (arXiv)

This paper presents a technical approach to robot learning of motor skills which combines active intrinsically motivated learning with imitation learning. Our architecture, called SGIM-D, allows efficient learning of high-dimensional continuous sensorimotor inverse models in robots, and in particular learns distributions of parameterised motor policies that solve a corresponding distribution of parameterised goals/tasks. This is made possible by the technical integration of imitation learning techniques within an algorithm for learning inverse models that relies on active goal babbling. After reviewing social learning and intrinsic motivation approaches to action learning, we describe the general framework of our algorithm, before detailing its architecture. In an experiment where a robot arm has to learn to use a flexible fishing line , we illustrate that SGIM-D efficiently combines the advantages of social learning and intrinsic motivation and benefits from human demonstration properties to learn how to produce varied outcomes in the environment, while developing more precise control policies in large spaces.