Robohub.org
 

Social learning


by
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.



tags:


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 :



Robots to the rescue: miniature robots offer new hope for search and rescue operations

  09 Sep 2025
Small two-wheeled robots, equipped with high-tech sensors, will help to find survivors faster in the aftermath of disasters.

#IJCAI2025 distinguished paper: Combining MORL with restraining bolts to learn normative behaviour

and   04 Sep 2025
The authors introduce a framework for guiding reinforcement learning agents to comply with social, legal, and ethical norms.

Researchers are teaching robots to walk on Mars from the sand of New Mexico

  02 Sep 2025
Researchers are closer to equipping a dog-like robot to conduct science on the surface of Mars

Engineering fantasy into reality

  26 Aug 2025
PhD student Erik Ballesteros is building “Doc Ock” arms for future astronauts.

RoboCup@Work League: Interview with Christoph Steup

and   22 Aug 2025
Find out more about the RoboCup League focussed on industrial production systems.

Interview with Haimin Hu: Game-theoretic integration of safety, interaction and learning for human-centered autonomy

and   21 Aug 2025
Hear from Haimin in the latest in our series featuring the 2025 AAAI / ACM SIGAI Doctoral Consortium participants.

AIhub coffee corner: Agentic AI

  15 Aug 2025
The AIhub coffee corner captures the musings of AI experts over a short conversation.

Interview with Kate Candon: Leveraging explicit and implicit feedback in human-robot interactions

and   25 Jul 2025
Hear from PhD student Kate about her work on human-robot interactions.



 

Robohub is supported by:




Would you like to learn how to tell impactful stories about your robot or AI system?


scicomm
training the next generation of science communicators in robotics & AI


 












©2025.05 - Association for the Understanding of Artificial Intelligence