First Plan Then Evaluate: Use a Vectorized Motion Planner for Grasping

2025-09-08

AI Summary

To address the trade-off between inefficient trajectory planning and inaccurate grasp success estimation in autonomous multi-fingered grasping, this paper proposes a “plan-then-evaluate” framework. It introduces, for the first time, a vectorized motion planner that enables parallel trajectory generation for multiple candidate grasp poses output by a grasp generator. The evaluation module computes a consistency score over the unified set of planned trajectories, eliminating the time–accuracy compromise inherent in conventional sequential frameworks—where suboptimal grasps are repeatedly re-planned or precision thresholds are relaxed. The framework is agnostic to both grasp generators and motion planners, enhancing system flexibility and real-time performance. Experiments demonstrate significantly higher grasp success rates than baseline methods across diverse objects and realistic settings (e.g., varying shelf and table heights), with strong generalization capability.

Technology Category

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

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Autonomous multi-finger grasping is a fundamental capability in robotic manipulation. Optimization-based approaches show strong performance, but tend to be sensitive to initialization and are potentially time-consuming. As an alternative, the generator-evaluator-planner framework has been proposed. A generator generates grasp candidates, an evaluator ranks the proposed grasps, and a motion planner plans a trajectory to the highest-ranked grasp. If the planner doesn't find a trajectory, a new trajectory optimization is started with the next-best grasp as the target and so on. However, executing lower-ranked grasps means a lower chance of grasp success, and multiple trajectory optimizations are time-consuming. Alternatively, relaxing the threshold for motion planning accuracy allows for easier computation of a successful trajectory but implies lower accuracy in estimating grasp success likelihood. It's a lose-lose proposition: either spend more time finding a successful trajectory or have a worse estimate of grasp success. We propose a framework that plans trajectories to a set of generated grasp targets in parallel, the evaluator estimates the grasp success likelihood of the resulting trajectories, and the robot executes the trajectory most likely to succeed. To plan trajectories to different targets efficiently, we propose the use of a vectorized motion planner. Our experiments show our approach improves over the traditional generator-evaluator-planner framework across different objects, generators, and motion planners, and successfully generalizes to novel environments in the real world, including different shelves and table heights. Project website https://sites.google.com/view/fpte

Problem

• Addresses sensitivity and time consumption in optimization-based grasping methods
• Solves the trade-off between trajectory planning time and grasp success estimation
• Enables efficient parallel trajectory planning for multiple grasp targets

Innovation

• Vectorized motion planner for parallel trajectory planning
• Evaluator assesses grasp success likelihood post-trajectory
• Executes highest success trajectory from parallel computations