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

OVERVIEW

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.

MOTIVATION

Dexterous manipulation requires precise control over both contact forces and motion trajectories.

• Force Control allows compliant interaction and high-frequency adaptation, but struggles with long-horizon manipulation.

• Trajectory Optimization enables obstacle-free path generation and coordinated motion, but cannot deal with dynamic interactions where contact forces become significant.

METHOD

To overcome these inherent limitations and achieve dexterous manipulation, we propose a multi-modal control framework that utilizes both force control and trajectory optimization in a switching manner based on the task needs:

1. Given the manipulation task, our approach decomposes it into one of the three types of subtasks: pure optimization, pure force control, or optimization-augmented force control (i.e. hybrid of optimization and force control).

2. Depending on the type of the subtask, the appropriate control method is applied.

As subtasks are achieved one after the other, the manipulation task is completed.

TASK DECOMPOSITION AND CONTROL STRATEGY

SYSTEM DIAGRAM

TEST CASES

What happens if only optimization or force control is applied, but not both?

Force and Pose Tracking Performance for the End Effectors (Bimanual box relocation case)