On the evolution of fingertip grasping manifolds

Abstract

Efficient and accurate planning of fingertip grasps is essential for dexterous in-hand manipulation. In this work, we present a system for fingertip grasp planning that incrementally learns a heuristic for hand reachability and multi-fingered inverse kinematics. The system consists of an online execution module and an offline optimization module. During execution the system plans and executes fingertip grasps using Canny's grasp quality metric and a learned random forest based hand reachability heuristic. In the offline module, this heuristic is improved based on a grasping manifold that is incrementally learned from the experiences collected during execution. The system is evaluated both in simulation and on a Schunk-SDH dexterous hand mounted on a KUKA-KR5 arm. We show that, as the grasping manifold is adapted to the system's experiences, the heuristic becomes more accurate, which results in an improved performance of the execution module. The improvement is not only observed for experienced objects, but also for previously unknown objects of similar sizes.

Fast, Anytime Motion Planning for Prehensile Manipulation in Clutter

“…Global heuristic search approaches aim to search the configuration space of a robotic arm given a discretization, frequently by focusing the search in the most promising subset or projection of that space [12][13] [14]. Heuristics that have been used in the context of manipulation tasks include reachability [15] or geometric task-based reasoning [16]. JIST first solves the manipulation problem for a freeflying end effector geometry, and then uses this as a heuristic during the search in the arm's configuration space.…”

Hierarchical Fingertip Space: A Unified Framework for Grasp Planning and In-Hand Grasp Adaptation

“…where C(·) ∈ R 6×(m−2) is an affine invariant encoding of m contacts in terms of its contact locations and normals [61]. Note that a smaller residual indicates more reachable contacts.…”