ArthroCut: Autonomous Policy Learning for Robotic Bone Resection in Knee Arthroplasty
ArthroCut: Autonomous Policy Learning for Robotic Bone Resection in Knee Arthroplasty
This research introduces ArthroCut, an autonomous policy learning framework designed to enhance the capabilities of knee arthroplasty robots.
The study presents a policy learning framework for robotic bone resection in knee arthroplasty, achieving an average success rate of 86% across six standard resections in bench-top trials using a multimodal dataset from 21 cases.
Source: arXiv Robotics — research abstracts · Read original article ↗
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What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
- Control
- Reported autonomousOpen source S2
Reported numbers
trajectories
23,205
Trajectory count
View original evidence
me-synchronized multimodal dataset from 21 complete cases (23,205 RGB--D pairs), integrating preoperative CT/MR, intraoperative NDI tracking of bones and end effector, RGB--D surgical video, robot state, and textual intent. The method operates on two complementary token families -- Preoperative Imaging Tokens (PIT) to encode patient-specific anatomy and planned resection planes, and Time-Aligned Surgical Tokens (TAST
Open source S3Reported trials
7 trials
Reported trials
View original evidence
) to fuse real-time visual, geometric, and kinematic evidence -- and emits an interpretable action grammar under grammar/safety-constrained decoding. In bench-top experiments on a knee prosthesis across seven trials, ArthroCut achieves an average success rate of 86% over the six standard resections, significantly outperforming strong baselines trained under the same protocol. Ablations show that TAST is the principal
Open source S4Reported success rate
86%
Reported trials
View original evidence
) to fuse real-time visual, geometric, and kinematic evidence -- and emits an interpretable action grammar under grammar/safety-constrained decoding. In bench-top experiments on a knee prosthesis across seven trials, ArthroCut achieves an average success rate of 86% over the six standard resections, significantly outperforming strong baselines trained under the same protocol. Ablations show that TAST is the principal
Open source S4cases
21
View original evidence
me-synchronized multimodal dataset from 21 complete cases (23,205 RGB--D pairs), integrating preoperative CT/MR, intraoperative NDI tracking of bones and end effector, RGB--D surgical video, robot state, and textual intent. The method operates on two complementary token families -- Preoperative Imaging Tokens (PIT) to encode patient-specific anatomy and planned resection planes, and Time-Aligned Surgical Tokens (TAST
Open source S3
Dataset details reported by the source
- Modalities
- CT/MR Open source S3NDI tracking Open source S3robot state Open source S3textual intent Open source S3
- Tasks
- knee arthroplasty Open source S2bone resection Open source S1preoperative imaging Open source S3
- Embodiments
- knee prosthesis Open source S4
- paper: not_reported
- code: not_reported
- weights: not_reported
- dataset: not_reported
What remains unknown
Not established in the collected evidence: Environment, Data origin.
Dataset licensing and commercial-use rights are not established here.
Reported performance applies to the described task. It does not establish general autonomy or deployment readiness.
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
arXiv:2603.03957v2 Announce Type: replace Abstract: Despite rapid commercialization of surgical robots, their autonomy and real-time decision-making remain limited in practice. To address this gap, we propose ArthroCut, an autonomous policy learning framework that upgrades knee arthroplasty robots from assistive execution to context-aware action generation. ArthroCut fine-tunes a Qwen--VL backbone on a self-built, ti
Open source S2
me-synchronized multimodal dataset from 21 complete cases (23,205 RGB--D pairs), integrating preoperative CT/MR, intraoperative NDI tracking of bones and end effector, RGB--D surgical video, robot state, and textual intent. The method operates on two complementary token families -- Preoperative Imaging Tokens (PIT) to encode patient-specific anatomy and planned resection planes, and Time-Aligned Surgical Tokens (TAST
Open source S3
ArthroCut: Autonomous Policy Learning for Robotic Bone Resection in Knee Arthroplasty
Open source S1
) to fuse real-time visual, geometric, and kinematic evidence -- and emits an interpretable action grammar under grammar/safety-constrained decoding. In bench-top experiments on a knee prosthesis across seven trials, ArthroCut achieves an average success rate of 86% over the six standard resections, significantly outperforming strong baselines trained under the same protocol. Ablations show that TAST is the principal
Open source S4
Implications for data suppliers
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- Check the exact dataset version, license, modalities and task coverage against the intended use.
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Source：arXiv Robotics — research abstracts · arxiv.org