Real-Time Online Re-Planning for Grasping Under Clutter and Uncertainty
This addresses the challenge of reliable robotic grasping in cluttered settings, which is incremental by improving upon existing open-loop planners.
The paper tackles the problem of grasping in cluttered environments by proposing an online re-planning approach to overcome failures from open-loop execution due to inaccurate modeling of dynamics and object properties. It achieves real-time re-planning cycles using a stochastic trajectory optimization algorithm, enabling reactive manipulation under uncertainty.
We consider the problem of grasping in clutter. While there have been motion planners developed to address this problem in recent years, these planners are mostly tailored for open-loop execution. Open-loop execution in this domain, however, is likely to fail, since it is not possible to model the dynamics of the multi-body multi-contact physical system with enough accuracy, neither is it reasonable to expect robots to know the exact physical properties of objects, such as frictional, inertial, and geometrical. Therefore, we propose an online re-planning approach for grasping through clutter. The main challenge is the long planning times this domain requires, which makes fast re-planning and fluent execution difficult to realize. In order to address this, we propose an easily parallelizable stochastic trajectory optimization based algorithm that generates a sequence of optimal controls. We show that by running this optimizer only for a small number of iterations, it is possible to perform real time re-planning cycles to achieve reactive manipulation under clutter and uncertainty.