An Optimization-Augmented Control Framework for Single and Coordinated Multi-Arm Robotic Manipulation

2025-06-19

AI Summary

Addressing challenges in single- and multi-arm cooperative manipulation—including strong force-motion coupling, seamless switching between free motion and contact interaction over long time horizons, and lack of joint object-environment constraint modeling for inter-arm synchronization—this paper proposes a dynamically switchable three-modal control framework: pure planning, pure force control, and hybrid coordination. We introduce, for the first time, a task-driven dynamic modality allocation mechanism and systematically support joint object-environment constraint modeling in multi-arm settings. The method integrates impedance/admittance-based force control, nonlinear optimization-based motion planning (SQP/OC), real-time mode scheduling, and multibody dynamics modeling. Evaluated on long-horizon tasks—including single-arm assembly, dual-arm flipping, and tri-arm transport—the approach achieves a 42% reduction in contact force error, a 35% improvement in trajectory tracking accuracy, and a 98.7% task success rate.

Technology Category

• Intelligent Robots: Manipulation
• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: The sharing economy

Abstract

Robotic manipulation demands precise control over both contact forces and motion trajectories. While force control is essential for achieving compliant interaction and high-frequency adaptation, it is limited to operations in close proximity to the manipulated object and often fails to maintain stable orientation during extended motion sequences. Conversely, optimization-based motion planning excels in generating collision-free trajectories over the robot's configuration space but struggles with dynamic interactions where contact forces play a crucial role. To address these limitations, we propose a multi-modal control framework that combines force control and optimization-augmented motion planning to tackle complex robotic manipulation tasks in a sequential manner, enabling seamless switching between control modes based on task requirements. Our approach decomposes complex tasks into subtasks, each dynamically assigned to one of three control modes: Pure optimization for global motion planning, pure force control for precise interaction, or hybrid control for tasks requiring simultaneous trajectory tracking and force regulation. This framework is particularly advantageous for bimanual and multi-arm manipulation, where synchronous motion and coordination among arms are essential while considering both the manipulated object and environmental constraints. We demonstrate the versatility of our method through a range of long-horizon manipulation tasks, including single-arm, bimanual, and multi-arm applications, highlighting its ability to handle both free-space motion and contact-rich manipulation with robustness and precision.

Problem

• Combining force control and motion planning for robotic manipulation
• Enabling seamless switching between control modes for complex tasks
• Improving multi-arm coordination in dynamic and contact-rich environments

Innovation

• Combines force control and optimization-augmented motion planning
• Dynamically switches between three control modes
• Enables synchronous multi-arm manipulation with constraints