Social learning

29 August 2010

share this:

Robots are portrayed as tomorrows helpers, be it in schools, hospitals, workplaces or homes. Unfortunately, such robots won’t be truly useful out-of-the-box because of the complexity of real-world environments and tasks. Instead, they will need to learn how to interact with objects in their environment to produce a desired outcome (affordance learning).

For this purpose, robots can explore the world while using machine learning techniques to update their knowledge. However, the learning process is sometimes saturated with examples of objects, actions and effects that won’t help the robot in its purpose.

In these cases, humans or other social partners can help direct robot learning (social learning). Most studies have focussed on scenarios where a teacher demonstrates how to correctly do a task. The robot then imitates the teacher by reproducing the same actions to achieve the same goals.

This approach, while being very efficient, typically means that the teacher needs to take time to train the robot, which can be burdensome. Furthermore, the robot might be so specialized for the demonstrated scenario that it will have trouble performing tasks that slightly differ. In addition, imitation only works when the teacher and robot have similar motion constraints and morphologies.

Luckily, humans and animals use a large variety of mechanisms to learn from social partners. Tapping into this reservoir, Cakmak et al. propose mechanisms where:
– robots interact with the same objects as the social partner (stimulus enhancement)
– robots try to achieve the same effect on the same object as the social partner (emulation)
– robots reproduce the same action as the social partner (mimicking)

Experiments performed in simulation compare stimulus enhancement, emulation, mimicking, imitation and non-social learning in a large variety of situations. The results summarize which mechanisms are better suited for which scenarios in a series of very useful guidelines. Demonstrations with two robots, Jimmy and Jane, were done to validate the study. Don’t miss the excellent video below for a summary of the article.

In the future, Cakmak et al. will focus on combining learning approaches to harness the full potential of this rich set of mechanisms.


Sabine Hauert is President of Robohub and Associate Professor at the Bristol Robotics Laboratory
Sabine Hauert is President of Robohub and Associate Professor at the Bristol Robotics Laboratory

Related posts :

Tesla’s Optimus robot isn’t very impressive – but it may be a sign of better things to come

Musk has now unveiled a prototype of the robot, called Optimus, which he hopes to mass-produce and sell for less than US$20,000 (A$31,000).
04 October 2022, by

Bipedal robot achieves Guinness World Record in 100 metres

Cassie the robot, developed at Oregon State University, records the fastest 100 metres by a bipedal robot.
03 October 2022, by and

Breaking through the mucus barrier

A capsule that tunnels through mucus in the GI tract could be used to orally administer large protein drugs such as insulin.
02 October 2022, by

Women in Tech leadership resources from IMTS 2022

There’ve been quite a few events recently focusing on Women in Robotics, Women in Manufacturing, Women in 3D Printing, in Engineering, and in Tech Leadership. One of the largest tradeshows in the US is IMTS 2022. Here I bring you some resources shared in the curated technical content and leadership sessions.
29 September 2022, by and

MIT engineers build a battery-free, wireless underwater camera

The device could help scientists explore unknown regions of the ocean, track pollution, or monitor the effects of climate change.
27 September 2022, by

How do we control robots on the moon?

In the future, we imagine that teams of robots will explore and develop the surface of nearby planets, moons and asteroids - taking samples, building structures, deploying instruments.
25 September 2022, by , and

©2021 - ROBOTS Association


©2021 - ROBOTS Association