Calibrating robots into their workspaces is crucial for manipulation tasks. Existing calibration techniques often rely on sensors external to the robot (cameras, laser scanners, etc.) or specialized tools. This reliance complicates the calibration process and increases the costs and time requirements. Furthermore, the associated setup and measurement procedures require significant human intervention, which makes them more challenging to operate. Using the built-in force-torque sensors, which are nowadays a default component in collaborative robots, this work proposes a self-calibration framework where robot-environmental spatial relations are automatically estimated through compliant exploratory actions by the robot itself. The self-calibration approach converges, verifies its own accuracy, and terminates upon completion, autonomously purely through interactive exploration of the environment's geometries. Extensive experiments validate the effectiveness of our self-calibration approach in accurately establishing the robot-environment spatial relationships without the need for additional sensing equipment or any human intervention.

翻译：将机器人标定到其工作空间对于操控任务至关重要。现有标定技术通常依赖于机器人外部传感器（如摄像头、激光扫描仪等）或专用工具，这一依赖性使得标定过程复杂化，并增加了成本与时间需求。此外，相关的设置与测量流程需要大量人工干预，进一步增加了操作难度。本文利用协作机器人当前标配的内置力-扭矩传感器，提出了一种自标定框架，通过机器人自身的柔顺探索动作自动估算机器人与环境之间的空间关系。该自标定方法能够在完全自主的状态下，仅通过与环境几何结构的交互式探索，实现精度收敛、自我验证并在完成后终止。大量实验验证了该自标定方法无需额外传感设备或任何人工干预即可准确建立机器人-环境空间关系的有效性。