We introduce the RoboPrec framework, where we: (i) develop a transpiler that integrates code transformations and robot-specific code generation with traditional numerical stability analysis methods (which calculate error bounds), and adapts them to be practical for robotics software; and then leverage this to (ii) generate guaranteed-accuracy fixed-point code that is deployable to embedded computing platforms. We use rigid body dynamics, a fundamental robotics workload, as a motivating case study. We find that RoboPrec-generated 32-bit fixed-point code can be up to 8x faster than float and 122x faster than double on embedded processors while, critically, also providing guaranteed accuracy bounds with lower worst-case error than float.