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<p>Despite decades of research in robotic manipulation, only a few autonomous manipulation skills are currently used. Traditional and machine-learning-based end-to-end solutions have shown substantial progress but still struggle to generate reliable manipulation skills for difficult processes like insertion or bending material. To facilitate the deployment and learning of tactile robot manipulation skills, we introduce here a taxonomy based on formal process specifications provided by experts, which assigns a suitable skill to a given process. We validated the inherent scalability of the taxonomy on 28 different skills from industrial application domains. The experimental results had success rates close to 100%, even under goal pose disturbances, with high performance attained by the skill models in terms of execution times and contact moments in partially known environments. The basic elements of the models are reusable and facilitate skill-learning to optimize control performance. Like established curricula for human trainees, this framework could provide a comprehensive platform that enables robots to acquire relevant manipulation skills and act as a catalyst to propel automation beyond its current capabilities.</p>

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A process-centric manipulation taxonomy for the organization, classification and synthesis of tactile robot skills

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月23日

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  • Lars Johannsmeier,
  • Samuel Schneider,
  • Yanan Li,
  • Etienne Burdet,
  • Sami Haddadin

摘要

Despite decades of research in robotic manipulation, only a few autonomous manipulation skills are currently used. Traditional and machine-learning-based end-to-end solutions have shown substantial progress but still struggle to generate reliable manipulation skills for difficult processes like insertion or bending material. To facilitate the deployment and learning of tactile robot manipulation skills, we introduce here a taxonomy based on formal process specifications provided by experts, which assigns a suitable skill to a given process. We validated the inherent scalability of the taxonomy on 28 different skills from industrial application domains. The experimental results had success rates close to 100%, even under goal pose disturbances, with high performance attained by the skill models in terms of execution times and contact moments in partially known environments. The basic elements of the models are reusable and facilitate skill-learning to optimize control performance. Like established curricula for human trainees, this framework could provide a comprehensive platform that enables robots to acquire relevant manipulation skills and act as a catalyst to propel automation beyond its current capabilities.