Robohub.org
 

iCub drums and crawls using bio-inspired control


by
17 January 2012



share this:

Ever see a lizard effortlessly run up a wall?

Like most vertebrates, lizards are able to quickly adapt to new environments in a robust way thanks to a special type of movement generator. The idea is that a high-level planner (the brain) is responsible for determining the key characteristics of a movement such as the position that needs to be reached by a limb or the amplitude and frequency with which the limbs should perform rhythmic motions. These high-level commands then serve as an input to motion primitives responsible for activating muscles in the correct sequence. Motion primitives are typically organized at the spinal level through neural networks called central pattern generators (CPGs).

This control architecture has many advantages for robotics. First, once the motion primitives are designed, only high-level commands are required to control the entire motion of the robot. Therefor, instead of planning the positions of all joints, the motion planner only needs to issue high-level goals such as “reach there” or “move your arm rhythmically with this amplitude and this frequency”. This greatly reduces the complexity of planning motions for robots with many degrees of freedom. Furthermore, CPGs are very fast, have low computational cost and can be modulated by sensory feedback in order to obtain adaptive behaviors.

Using this control architecture, Degallier et al. were able to turn the iCub humanoid seen in the video below into an on-demand drummer. Random users at a robotics conference were able to change on-line a score that the iCub was playing or test how well it could adapt when its drums were moved. To show the generality of their approach, they then applied the same architecture to make the iCub crawl and reach for objects. Although one behaviour was rhythmic (crawling) and the other discrete (reaching), the robot was easily able to switch between the two.



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 :



Robot Talk Episode 123 – Standardising robot programming, with Nick Thompson

  30 May 2025
In the latest episode of the Robot Talk podcast, Claire chatted to Nick Thompson from BOW about software that makes robots easier to program.

Congratulations to the #AAMAS2025 best paper, best demo, and distinguished dissertation award winners

  29 May 2025
Find out who won the awards presented at the International Conference on Autonomous Agents and Multiagent Systems last week.

Congratulations to the #ICRA2025 best paper award winners

  27 May 2025
The winners and finalists in the different categories have been announced.

#ICRA2025 social media round-up

  23 May 2025
Find out what the participants got up to at the International Conference on Robotics & Automation.

Robot Talk Episode 122 – Bio-inspired flying robots, with Jane Pauline Ramos Ramirez

  23 May 2025
In the latest episode of the Robot Talk podcast, Claire chatted to Jane Pauline Ramos Ramirez from Delft University of Technology about drones that can move on land and in the air.

Robot Talk Episode 121 – Adaptable robots for the home, with Lerrel Pinto

  16 May 2025
In the latest episode of the Robot Talk podcast, Claire chatted to Lerrel Pinto from New York University about using machine learning to train robots to adapt to new environments.

What’s coming up at #ICRA2025?

  16 May 2025
Find out what's in store at the IEEE International Conference on Robotics & Automation, which will take place from 19-23 May.

Robot see, robot do: System learns after watching how-tos

  14 May 2025
Researchers have developed a new robotic framework that allows robots to learn tasks by watching a how-to video



 

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


 












©2025.05 - Association for the Understanding of Artificial Intelligence