We describe a spline-based framework for incorporating both position and su
rface-normal information into estimates of curvature based shape parameters
. Such position and normal data can be obtained with different types of con
tact sensors, for example, tactile sensors, force-moment sensors, etc. The
heart of our framework is an extended B-spline formulation that incorporate
s the surface-normal information into a B-spline surface fit of the positio
n data. The surface-normal information provides additional constraints for
the surface fit and can also significantly improve the approximation of the
surface. Curvature-based shape parameters applied to this B-spline surface
are then used to characterize the local shape of the object surface. Preli
minary experiments with simple primitives-spherical, cylindrical, and plana
r shapes-and an off-the-shelf force/moment sensor to obtain the position an
d surface-normal data show that despite coarse resolution of the sensor thi
s approach succeeds in qualitative shape recognition.