Manipulation planning with soft task constraints

We present a randomized configuration space planner that enforces soft workspace task constraints. A soft task constraint allows an interval of feasible values while favoring a given exact value. Previous work only allows for enforcing an exact value or an interval without a specific preference. Soft task constraints are a useful concept in everyday life. For example when carrying a container of liquid we want to keep it as close to the upright position as possible but want to be able to tilt it slightly in order to avoid obstacles. This paper introduces the necessary algorithms for handling such constraints, including projection methods and useful representations of everyday constraints. Our algorithms are evaluated on a series of simulated benchmark problems and shown to yield significant improvement in constraint satisfaction.