HAD-TAMP: Human adaptive task and motion planning for human–robot collaboration in industrial scenario

Abstract

Task and Motion Planning (TAMP) is essential for efficient Human–Robot Collaboration (HRC) in industrial settings, yet existing approaches struggle to handle human interventions and dynamic environments. This paper presents a Human Adaptive Task and Motion Planning (HAD-TAMP) framework that seamlessly integrates human pose and actions into the planning process to quickly adapt to human requests or deviations from the process plan. The framework consists of three key modules: a task planning module, which generates and updates task sequences based on real-time human input, a motion planning module composed of a set of motion planners specialized for different phases of the collaboration (e.g., collaborative transportation of materials), and a context reasoner module which coordinates the overall process based on the sensory information available. A key contribution is using a receding horizon strategy, enabling real-time adaptation to human inputs and environmental changes. The approach is validated in a real industrial HRC scenario through two applications: gesture-based human–robot interaction and close human–robot collaboration in carbon fiber draping. Experimental results demonstrate the framework’s effectiveness in ensuring adaptability to multiple human requests and efficiency: the re-planning time is 4 times and 5 times faster than the generation of a new plan. • A Human Adaptive TAMP framework that dynamically integrates human pose and actions. • A Context-Reasoning module that optimizes and supervises the overall process. • Adaptive re-planning strategy based on a receding horizon approach to minimize delays. • Experimental validation in two real industrial HRC scenarios.