Efficient Grasp Planning and Execution with Multi-Fingered Hands by Surface Fitting
This addresses the challenge of efficient and robust grasp planning for robotic manipulation, but appears incremental as it builds on existing gradient-based methods.
The paper tackles the problem of planning grasps with multi-fingered hands by introducing a framework that uses multi-dimensional iterative surface fitting and grasp trajectory optimization to find optimal contact regions and finger trajectories, verified through simulations and experiments.
This paper introduces a framework to plan grasps with multi-fingered hands. The framework includes a multi-dimensional iterative surface fitting (MDISF) for grasp planning and a grasp trajectory optimization (GTO) for grasp imagination. The MDISF algorithm searches for optimal contact regions and hand configurations by minimizing the collision and surface fitting error, and the GTO algorithm generates optimal finger trajectories to reach the highly ranked grasp configurations and avoid collision with the environment. The proposed grasp planning and imagination framework considers the collision avoidance and the kinematics of the hand-robot system, and is able to plan grasps and trajectories of different categories efficiently with gradient-based methods using the captured point cloud. The found grasps and trajectories are robust to sensing noises and underlying uncertainties. The effectiveness of the proposed framework is verified by both simulations and experiments.