SAPS: Shared Autonomy for Policy Steering by Blending Teleoperation with a Pretrained VLA
SAPS: Shared Autonomy for Policy Steering by Blending Teleoperation with a Pretrained VLA
This research proposes SAPS, a lightweight framework that merges real-time human teleoperation commands with pretrained VLA actions to enhance robot manipulation reliability. Evaluated across simulated and real-world scenarios, SAPS achieves up to 77.4% hardware success, with reduced human intervention and workload, demonstrating its effectiveness for real-world deployment and assistive teleoperation.
This paper introduces SAPS, a framework that combines human teleoperation with pretrained VLA models to improve reliability in robot manipulation. The approach achieves higher success rates than full autonomy or standalone teleoperation, with reduced human intervention and workload, suggesting potential for real-world deployment and assistive applications.
Source: arXiv Robotics — research abstracts · Read original article ↗
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actions. SAPS operates purely at the action level, requiring no policy retraining, auxiliary dynamics models, or architectural modifications. We evaluate three arbitration strategies across simulated (LIBERO, CALVIN) and real-world evaluations. In a 14-person user study, SAPS achieved up to 77.4% hardware success, compared with 8.3% for full autonomy and 34.5% for teleoperation. Furthermore, active human intervention
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was required only 45% of the time, reducing NASA-TLX workload by 20.8% in simulation and 28.2% on hardware. A case study with an expert user with quadriplegia similarly demonstrated matched or improved success while reducing completion time. These results highlight SAPS as a highly effective, model-agnostic approach for reliable real-world deployment and assistive teleoperation. A website with additional details is
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Source：arXiv Robotics — research abstracts · arxiv.org