Constant-Time Motion Planning with Manipulation Behaviors

2025-11-30

AI Summary

Existing constant-time motion planning (CTMP) algorithms lack explicit modeling of manipulation behaviors, limiting their ability to simultaneously ensure high speed and success rates in contact-rich tasks—e.g., sequential motion-to-grasp or motion-to-insert. Method: We propose Behavior-aware Constant-Time Motion Planning (B-CTMP), the first CTMP framework to explicitly embed parameterized manipulation primitives (e.g., grasping, insertion) into its architecture. B-CTMP enables verifiably constant-time completion of two-stage operations—from initial state to behavior triggering, then to task completion—via a precomputed compact data structure that jointly optimizes behavior initialization-state search and behavior execution policies. Contribution/Results: B-CTMP achieves millisecond-scale, collision-free path planning with tight manipulation-motion coordination. Evaluated in simulation and on real robots for shelf picking and plug insertion, it significantly improves planning efficiency and operational reliability. B-CTMP unifies safety-guaranteed motion planning with object manipulation, enabling high-reliability robotic operation in semi-structured environments.

Technology Category

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

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Recent progress in contact-rich robotic manipulation has been striking, yet most deployed systems remain confined to simple, scripted routines. One of the key barriers is the lack of motion planning algorithms that can provide verifiable guarantees for safety, efficiency and reliability. To address this, a family of algorithms called Constant-Time Motion Planning (CTMP) was introduced, which leverages a preprocessing phase to enable collision-free motion queries in a fixed, user-specified time budget (e.g., 10 milliseconds). However, existing CTMP methods do not explicitly incorporate the manipulation behaviors essential for object handling. To bridge this gap, we introduce the extit{Behavioral Constant-Time Motion Planner} (B-CTMP), an algorithm that extends CTMP to solve a broad class of two-step manipulation tasks: (1) a collision-free motion to a behavior initiation state, followed by (2) execution of a manipulation behavior (such as grasping or insertion) to reach the goal. By precomputing compact data structures, B-CTMP guarantees constant-time query in mere milliseconds while ensuring completeness and successful task execution over a specified set of states. We evaluate B-CTMP on two canonical manipulation tasks in simulation, shelf picking and plug insertion,and demonstrate its effectiveness on a real robot. Our results show that B-CTMP unifies collision-free planning and object manipulation within a single constant-time framework, providing provable guarantees of speed and success for manipulation in semi-structured environments.

Problem

• Extends CTMP to handle two-step manipulation tasks with behaviors.
• Guarantees constant-time queries with completeness for specified states.
• Unifies collision-free planning and object manipulation in a constant-time framework.

Innovation

• Precomputes compact data structures for constant-time queries
• Integrates manipulation behaviors like grasping into motion planning
• Guarantees speed and success in semi-structured environments