🤖 AI Summary
This work addresses the lack of reusable, cross-platform compliant control infrastructure in existing robotic software, which hinders unified algorithm implementation and high-level interfacing. The authors propose a robot-agnostic, modular compliance control framework that decouples controller infrastructure from control laws via a plugin architecture. It supports variable impedance control in both joint and Cartesian spaces and, for the first time, enables online adaptation of the primary compliance direction according to task geometry—overcoming the limitations of fixed coordinate frames. Built upon the ROS ecosystem, the framework leverages Pinocchio to parse URDF models for kinematic and dynamic computations and employs runtime plugin loading with generic wrappers to interface heterogeneous hardware. Real-world and simulated experiments demonstrate significant performance improvements in contact-intensive tasks and seamless transferability across multiple robotic arms.
📝 Abstract
The paper proposes a robot-agnostic compliant-control framework that extends the ROS control ecosystem with standardized joint and Cartesian command interfaces. It addresses a key limitation of existing control software: no reusable infrastructure for implementing compliant-control algorithms across different manipulators while preserving a common interface to higher-level applications. A plugin-based architecture separates controller infrastructure from control-law implementation. Generic wrappers use existing hardware abstractions to interface with different manipulators, while runtime-loaded plugins implement only the control law. Command interfaces support joint- and Cartesian-space references, stiffness and damping gains, nullspace targets, and feedforward terms, enabling variable impedance and diverse compliant-control formulations. Robot kinematics and dynamics are computed from URDF models using Pinocchio. The architecture facilitates the development of compliant-control strategies and enables the same implementation to be deployed across platforms unchanged. The complete framework, including reference controllers, high-level task interfaces, and example configurations for various manipulators, is open-sourced. The reference Cartesian impedance controller supports task-dependent compliance by rotating translational and rotational stiffness and damping, allowing the principal compliance directions to be updated online according to local task geometry rather than remaining fixed in the robot base or TCP frame. This is particularly important in contact-rich manipulation, where the desired directions of motion, constraints, and compliance directions may vary throughout task execution. Real-robot experiments demonstrate task-dependent compliance in contact-rich manipulation, while simulations show portability across manipulators with distinct kinematic and dynamic characteristics.