Puppet Master

I have been fascinated with Physics-based animation for a while now. Particularly with Soft Body Mechainics. I started learning mass-spring simulators a few months ago. A couple of days ago, I came across a fascinating research area in the field of physics-based animation and robotics which deals with the simulation of real-world puppets.

A group of scientists at ETH Zurich introduced us to a robotic system that simulates real-world string puppets or marionettes. “PuppetMaster” can operate simple puppets like a puppy, snake, or even a Chinese dragon! The goal of the project can be summarized as follows.

To Generate highly dynamic motion trajectories for string-driven physical systems using dexterous robots as puppeteers.

The challenging part of such a project is the unpredictable linkage of puppet motion with the puppeteer’s movement.

Overview

The system is modeled to take the following inputs.

As seen in the following diagram, x represents the motion trajectory of the marionette and p is the control variable for the robot’s motion.

Puppet Master components

Thus, x can be expressed as an implicit function of p. Now based on Newton’s second law of motion we arrive at the following equation.

G(x, p) = 0

Furthermore, an objective function is modeled to represent the degree of resemblance of the marionette’s motion to it’s target and smoothness of the control actions.

O(x( p ), p)

From here on, the aim is to minimize p. First order sensitivity analysis provides an analytical expression for the Jacobian dx/dp which enables the use of first-order gradient-based methods. Unfortunately, the first order optimization converges far too slow. Hence, a second-order derivative of O with respect to p i.e. Hessian is needed for Newton’s method.

Even though Newton’s method converges faster than gradient descent, it requires evaluating the second-order sensitivity term and regularization of the Hessian. As an improvement to this, the researchers chose to go with a Gauss-Newton approximation for the Hessian.

Finally, an optimization algorithm is used to get the optimal p values using the Hessian or its approximation. For this, we solve for d using, Hd = -dO/dp, and then run a backtracking line search to find the step size to evaluate O(x(p), p).

Limitations

While this research has laid a great foundation for future robotic puppeteering endeavors, researchers admit that this is just the first step in endowing robots with human-level skills. There are certain areas of improvements, some of these are,

Applications

The project aims to enable robots to manipulate various types of complex physical systems from manufacturing to hospitals or construction sites.

Conclusion

In this work, we saw how to solve the puppeteering control problem using a second-order trajectory optimization method by leveraging the derivatives of motions(computed using sensitivity analysis) generated by a forward dynamics simulation to predict how the robot’s actions will affect the motion of the marionette.