Synthesizing Grasps and Regrasps for Complex Manipulation Tasks

2025-01-30

AI Summary

Complex dexterous manipulation tasks—such as axial rotation—involve time-varying dynamics, necessitating multi-stage grasping and repeated regrasping, which challenge conventional grasp planning frameworks. Method: This paper proposes a point-cloud-driven closed-loop grasp planning method. It formalizes complex manipulation as a sequence of constant screw motions and automatically determines regrasp timing and count via intersection analysis of graspable regions. The method integrates point-cloud-based geometric modeling, screw-motion-constrained trajectory optimization, and grasp quality evaluation to synthesize continuous grasp–regrasp sequences under path constraints. Results: Evaluated on real-world RGB-D point cloud data, the approach robustly generates feasible multi-segment screw-motion manipulation sequences. It significantly improves task success rates for intricate operations and overcomes the limitations of traditional “grasp-and-place” paradigms.

Technology Category

• Intelligent Robots: Manipulation
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

In complex manipulation tasks, e.g., manipulation by pivoting, the motion of the object being manipulated has to satisfy path constraints that can change during the motion. Therefore, a single grasp may not be sufficient for the entire path, and the object may need to be regrasped. Additionally, geometric data for objects from a sensor are usually available in the form of point clouds. The problem of computing grasps and regrasps from point-cloud representation of objects for complex manipulation tasks is a key problem in endowing robots with manipulation capabilities beyond pick-and-place. In this paper, we formalize the problem of grasping/regrasping for complex manipulation tasks with objects represented by (partial) point clouds and present an algorithm to solve it. We represent a complex manipulation task as a sequence of constant screw motions. Using a manipulation plan skeleton as a sequence of constant screw motions, we use a grasp metric to find graspable regions on the object for every constant screw segment. The overlap of the graspable regions for contiguous screws are then used to determine when and how many times the object needs to be regrasped. We present experimental results on point cloud data collected from RGB-D sensors to illustrate our approach.

Problem

• Robot Grasping
• Point Cloud Data
• Complex Manipulation Tasks

Innovation

• Grip and Regrip Method
• Point Cloud Data
• Complex Manual Tasks