Will One Small Step for AI Be One Giant Leap for Robotics?

Robot learns to walk by itself using artificial intelligence.

Have you ever wondered how human-like a robot can become? Initially, in the learning phase, a tendon-driving robotic limb undergoes a motor babbling phase where the system attempts random control sequences and gathers the associated kinematics. The input-output data from the motor babbling is fed to a multi-layer perceptron artificial neural network (ANN) to train it. In turn, the trained ANN produces an initial output-input (inverse) map based on the system’s dynamics.

The ANN of the inverse map from 6D kinematics to a 3D motor control sequences has three layers and twenty-four nodes total. There are six nodes in the input layer, fifteen nodes in the hidden layer, and three nodes in the output layer. The hyperbolic tangent sigmoid transfer function was used to compute a layer’s output from its net input—well suited for neural networks when velocity is a priority over the precise shape of the transfer function. Scaling was used for the output layer.  

    

The next phase refines the initial learning and consists of two parts—exploration and converging toward high reward. Exploring random attempts over time will result in solutions with a treadmill reward. Then, the behaviour is reinforced with a reward to refine the inverse map. The ANN’s weights are adjusted between attempts to enable the system to learn from experience.

The results are the G2P algorithm is able to learn how to propel a treadmill autonomously—without explicit modelling of the dynamics or closed-loop error sensing. In effect, the researchers created robots that learn from experience and experimentation, rather than explicit instructions or prior simulation guidance. “May lead to a class of robots with unique advantages in terms of design, versatility, and performance,” and “contributes to computational neuroscience by providing a biologically and developmentally tenable learning strategy for anatomically plausible limbs.”