Efficient Planning for Near-Optimal Compliant Manipulation Leveraging Environmental Contact

Abstract

Path planning classically focuses on avoiding environmental contact. However, some assembly tasks permit contact through compliance, and such contact allows for more efficient and reliable solutions under action uncertainty. But optimal manipulation plans that leverage environmental contact are difficult to compute. Environmental contact produces complex kinematics, that are difficult to plan on. This complexity is usually addressed by discretization over state and action space, but quickly becomes computationally intractable. To overcome these challenges, we use the insight that only actions on configurations near the contact manifold are likely to involve complex kinematics, while segments of the plan through free space do not. Leveraging this structure can greatly reduce the number of states considered and scales much better with problem complexity. We develop an algorithm based on this idea and show that it performs comparably to full MDP solutions at a fraction of the computational cost.