Failure-Aware Adaptive Grasping: Learning from Surgical Complications to Improve Robotic Manipulation

While most robotic grasping research focuses on success metrics, the surgical literature reveals fascinating failure patterns. Maman et al.'s 2024 study showed robotic lumbar fusion had unexpected complications like increased anemia and acute kidney injury despite overall better outcomes. This suggests robotic systems can create novel failure modes through their unique interaction dynamics. I propose building a "failure-aware" grasping system that learns from these medical complications to predict when a grasp might fail in non-surgical contexts. Unlike Alvanpour et al.'s 2025 work that explains failures post-hoc, this approach would use surgical complication patterns as training data to pre-emptively adjust grasp strategies. The system would map surgical complications (like tissue trauma from excessive force) to analogous robotic manipulation failures (like object damage or slip), creating a predictive model that could revolutionize how robots handle delicate or uncertain objects.

References:

1. Comparative evaluation of postoperative outcomes and expenditure between robotic and conventional single-level lumbar fusion surgery: a comprehensive analysis of nationwide inpatient sample data.. D. Maman, A. Mahamid, Binyamin Finkel, Hadar Gan-Or, Linor Fournier, Y. Berkovich, E. Behrbalk (2024). European spine journal.
2. Comparative Analysis of Post Hoc Explainable Methods for Robotic Grasp Failure Prediction. Aneseh Alvanpour, Cagla Acun, Kyle Spurlock, Christopher K. Robinson, Sumit K. Das, Dan O. Popa, Olfa Nasraoui (2025). Electronics.