IROSA: Interactive Robot Skill Adaptation using Natural Language

Foundation models have demonstrated impressive capabilities across diverse domains, while imitation learning provides principled methods for robot skill adaptation from limited data. Combining these approaches holds significant promise for direct application to robotics, yet this combination has received limited attention, particularly for industrial deployment. We present a novel framework that enables open-vocabulary skill adaptation through a tool-based architecture, maintaining a protective abstraction layer between the language model and robot hardware. Our approach leverages pre-trained LLMs to select and parameterize specific tools for adapting robot skills without requiring fine-tuning or direct model-to-robot interaction. We demonstrate the framework on a 7-DoF torque-controlled robot performing an industrial bearing ring insertion task, showing successful skill adaptation through natural language commands for speed adjustment, trajectory correction, and obstacle avoidance while maintaining safety, transparency, and interpretability.

Interactive Robot Programming for Surface Finishing via Task-Centric Mixed Reality Interfaces

Lengthy setup processes that require robotics expertise remain a major barrier to deploying robots for tasks involving high product variability and small batch sizes. As a result, collaborative robots, despite their advanced sensing and control capabilities, are rarely used for surface finishing in small-scale craft and manufacturing settings. To address this gap, we propose a novel robot programming approach that enables non-experts to intuitively program robots through interactive, task-focused workflows. For that, we developed a new surface segmentation algorithm that incorporates human input to identify and refine workpiece regions for processing. Throughout the programming process, users receive continuous visual feedback on the robot's learned model, enabling them to iteratively refine the segmentation result. Based on the segmented surface model, a robot trajectory is generated to cover the desired processing area. We evaluated multiple interaction designs across two comprehensive user studies to derive an optimal interface that significantly reduces user workload, improves usability and enables effective task programming even for users with limited practical experience.

Capability-based Frameworks for Industrial Robot Skills: a Survey

The research community is puzzled with words like skill, action, atomic unit and others when trying to describe robot capabilities. However, for giving the possibility to integrate such in the industrial scenario a standardization of their description is necessary. This work, through a structured review approach, tries to identify commonalities in the research community. From this review it was possible to perceive that most of the industrially focused research work targets simple capabilities like pick and place, the large amount of authors agree on a structure consisting of task, skill and primitive, the Robot Operating System is a common framework both in industrial and non-industrial domains and skills are a main enabler for high mix - low volume productions.