Adaptive Human-Robot Collaborative Painting Combining Preference-Based Optimization and Dynamic Motion Primitives
2026-08-03 • Robotics
Robotics
AI summaryⓘ
The authors created a system where a robot helps people with tasks like painting by learning from their preferences and movements. The robot watches how the person moves their hand and adjusts the object being worked on in real-time to follow those movements better. They use a special method (Preference-Based Optimization) to gather feedback from the user and improve the robot's settings like speed and responsiveness over time. They tested this with different people and found it made the work easier for the person while improving the overall task.
Preference-Based OptimizationDynamic Movement PrimitivesGLISp algorithmrobot-assisted taskshuman-robot collaborationergonomicsreal-time adaptationcontrol parameterspainting task optimization
Authors
C. Cella, M. Ristic, M. Faroni, A. M. Zanchettin, P. Rocco
Abstract
This work presents a human-centered collaborative framework that integrates Preference-Based Optimization (PBO) and Dynamic Movement Primitives (DMPs) to optimize robot-assisted tasks such as painting. The system allows the operator to perform the process while the robot adapts its behavior in real-time, dynamically adjusting the orientation of the piece in order to match the orientation of the operator's hand. The PBO framework leverages the GLISp algorithm to iteratively refine control parameters such as execution time, robot responsiveness, and rotation amplification through human feedback. Moreover, DMPs have been modified to enhance the reactive behavior of the robot and its adaptability to ergonomic requirements. The method was validated with a heterogeneous group of participants executing \rev{painting tasks}. The results show that our strategy effectively reduces operator effort while optimizing process outcomes.