Single Point of Contact Manipulation of Unknown Objects Stuart Anderson Advisor: Reid Simmons School of Computer Science Carnegie Mellon University

Overview Problem motivation Task Description High level first pass Detailed pass Results and Conclusions

Motivation There are many applications for the ability to manipulate in the presence of uncertainty about the object being manipulated: –Mars Rover Rock Scenario –Possible Nanomanipulation Applications? –General autonomy

The Problem Given an object with unknown geometric and dynamic parameters, how can a robot discover these parameters? A six degree of freedom fiducial (pose sensor) is affixed to the object. We can manipulate the object by applying forces to its surface. Applied forces and fiducial observations have white gaussian noise

How can we learn about the world? Experimentation –Based on a belief about the state of the world make a prediction of what will happen when a particular action is taken. Then observe what really happens and incorporate this new information into the belief.

Simple Physics Consider a single particle in space. –The only unknown parameter is mass. –We represent our uncertainty about the mass using a probability distribution over potential masses. –We push the object with a given force, and believe we will observe a given acceleration. –Compute a new uncertainty (or belief) distribution based on this observation

Simple Physics Suppose the object can weigh 1 or 2 kg. – p(1kg)=.9, p(2kg)=.1. We push with 2N of force. –Suppose we observe an acceleration of 1.

Simple Physics Over many observations we expect the distribution to converge to the true value of the mass.

Learning Two approaches –Extensions to the Kalman filter –Particle filter What do we learn about the system? –Geometry pre-selected set of normals learn their offsets, define a convex hull –Dynamics Parameters Mass, Inertial Tensor, Center of Mass, Coefficient of Friction

Physics The motion of a rigid body is described by the Newton-Euler equations. For simplicity, we use the term ‘wrench’ to describe vector composed of a force and a torque. We also introduce a generalize mass matrix. This allows us to write

Physics In order to predict the motion of an object, we need to know the forces applied to it. If we have some belief about the parameters of the object, then we can explicitly compute the wrench due to the manipulator and to gravity

Physics The contact wrench cannot be computed explicitly. The pressure at any given point is indeterminate and both the normal force and frictional force depend on it. This severely limits what can be known about the contact wrench

Physics

So the total contact wrench falls within the convex hull of the wrenches produced by applying all pressure at a single point.

Physics We need to know more about these convex hulls. –The frictional load is modeled by Coulomb friction. The upper bound on the magnitude of the frictional load is linear in the contact pressure The direction of the frictional load at a given point is the direction that point is sliding. If the point is not sliding then the load direction is not known. Thus, when the object is sliding we have

Physics If the motion is a pure translation, then the direction of motion is constant for all points and So the set of w’(x) where x is the vertices of the convex hull of the contact region defines the convex hull of possible wrenches, and lies within a two dimensional slice of wrench space.

Physics If the motion is a generalized rotation then the direction of the frictional load is no longer linear in position. –The velocity of points on the surface is linear in X though.

Physics Since distance is linear in polar coordinates, we apply a change of coordinates to x. How do we visualize these equations?

Physics Developing an exact description of the convex wrench hull in this case is difficult. Instead, we take samples of w’(x) and show bounds on the error in the convex hull constructed from these samples. –Because w’(x) is linear in r we need only sample the minimal and maximal values of r for a given theta. –The number of samples needed for a given maximum error bound grows linearly in the radius of a circle which encloses the contact hull.

Physics Finally, when the object is not moving we take additional samples to model the indeterminacy in the direction of the frictional load.

Observations and Predictions We observe accelerations based on the fiducial movement. –Caveat: assume we take observations frequently enough that the forces applied to the object remain about constant. We can stop looking when bad stuff (impact) happens. –So we need to predict the acceleration based on state and force applied. –In general, we can’t get an explicit answer to this question since the contact pressure distribution is undetermined. –But we can compute bounds on the resulting contact force and torque based on the velocity, geometry, and applied force. We also observe the pose of the object, and use that to refine geometric estimates.

Particle Filter Approximate the posterior distribution using a set of particles. –What’s a particle? A description of the system – a state vector –Lets us work with any system, don’t need to worry about linearization. –But we have to be careful or dimensionality becomes a problem. m(1), I(6), mu(1), cm(3), x(3), v(3),geom(~250)

Particle Filter Approach Given a particle b, when we make an observation o Then resample the distribution based on the relative likelihood of each particle.

Particle Filter We can’t use a standard particle filter with our system, since it is not possible to compute p(o|b) exactly. –We let each particle have an upper and lower bound on its probability, instead of an explicit value. –But how to normalize the upper bound after an observation?

Particle Filter

How do we compute a new upper bound for a given particle? –We must compute the upper bound over all possible distributions.

Particle Filter We can solve a component of this by quadratic programming.

Particle Filter The minimum probability is easier –Try all wrenches on facets of the convex hull, choose the furthest.

Particle Filter But how to optimize? There are a limited number of minima and maxima, and we can find them (sort of) –Root finding on a sum of exponential functions with known coefficients.

Planning and Behaviors Planning has a problem with dimensionality too. –The set of actions is a (potentially unconnected) path through the set of all forces at all points. –Behaviors are a way to deal with this Slide (maintaining current contact face) Switch contact face (a.k.a roll)

Uncertainty Given a set of particles, we can simulate a behavior many times, assuming a different particle to be the ‘truth’ each time. We can look at the statistics of these trials to see what effect a given behavior will have on the belief state. –Some behaviors tend to reduce the variance or entropy of the distribution. Pick these.

Uncertainty But we can only compute bounds on these statistics. –If sliding has reward 1 with P=[.2,.8] and rolling has reward 4 with P=[.1,.6], which is the correct choice? Is there a correct choice?

Does it work? Sort of/not yet! Computing p(o|b) works, behaviors work, but the non-linear optimization step in the particle filter doesn’t work yet. Currently, the computed bounds on distributions include some distributions which are not possible given the observations, and the filter does not converge.

Will it work? I hope! But maybe the decision to treat the pressure distribution as totally unknown is unreasonable. –But not unjustified under a rigid body model! –Humans are very compliant –Everything else is sort of compliant –What if we worked on a rubber mat? What about trying to model the behavior that the physical model doesn’t describe? –Lynch did some work on estimating friction that might be a start to this. However, it would be very hard to make it work with changing geometric estimates. –What else could we try?

Other future work In the planner, we should select a small subset of ‘representative particles’ from the distribution and only use these for computing aggregate statistics. Rao-Blackwellization in the particle filter for parameters with well defined effects or no effects on the w’ hull.

The Software Dynamics Simulation / Visualization –Human Interface –Planning Interface Geometric Reasoning –We can refine the convex hull and find the predicted contact hull. Wrench Reasoning –We can compute the probability of an observation within known and controllable error bounds for all motion cases. Particle Filter –Working implementation of the particle filter based on an incorrect assumption about the upper bound update. Behaviors –Slide, shift face. Reactive control is a little too good because the force can be shifted instantaneously.