Hardware-Agnostic Imitation Learning Method for Autonomous Ultrasound Scanning Addressing Physical Deployment Discrepancies
Year: 2026
Type: article
Abstract: To achieve autonomous ultrasound scanning skill transfer across different physical equipment instances and address the limitations of traditional imitation learning methods-which struggle with cross-instance generalization due to their reliance on specific manipulator parameters-this study proposes a physical-parameter-decoupled imitation learning method based on waypoint representation. This approach utilizes a greedy algorithm to automatically extract key nodes within the task space from expert demonstration trajectories, constructing a trajectory representation decoupled from low-level kinematic parameters and base calibration errors. Simultaneously, a velocity-aware adaptive error precision adjustment mechanism is introduced to dynamically modulate waypoint extraction thresholds, simulating the speed-accuracy strategies employed by sonographers across different scanning phases. Cross-validation across two mainstream generative architectures-Action Chunking Transformer (ACT) and Diffusion Policy-on an offline dataset confirms the plug-and-play capability of waypoint representation in suppressing long-horizon error accumulation, with both architectures achieving significant reductions in prediction errors. For physical deployment, a complete ACT-waypoint system featuring low-level triple safety redundancy was validated. In kidney long-axis standard plane scanning tasks, the system achieved a 92% success rate on the source domain manipulator and maintained an 84% success rate on the target deployment manipulator, despite incompatible low-level kinematic parameters and base coordinates. Force control accuracy remained stable around the target value of 12 N. The results demonstrate that the proposed method effectively overcomes base coordinate and D-H parameter discrepancies to achieve cross-instance zero-shot skill transfer, significantly enhancing the adaptability across physical instances and the scanning success rate of imitation learning models.
Source: Sensors
DOI: 10.3390/s26092804
Institution: Xi'an University of Science and Technology
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W7160034316
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