Research map / Reinforcement learning / RL from feedback
SurgicAI: A Fine-grained Platform for Data Collection and Benchmarking in Surgical Policy Learning
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only. Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Introduces SurgicAI, a platform for fine-grained data collection and benchmarking in surgical policy learning.
Problem
Surgical policy learning lacks fine-grained data collection and standardized benchmarking infrastructure for training and evaluating embodied surgical agents.
Approach
A platform that supports fine-grained surgical data collection together with benchmarking for surgical policy learning; the abstract gives no further technical detail.
Closest papers by meaning
- SurgicAI: A Hierarchical Platform for Fine-Grained Surgical Policy Learning and Benchmarking (NeurIPS 2024 · similarity 0.88)
- SurgCoT: Advancing Spatiotemporal Reasoning in Surgical Videos through a Chain-of-Thought Benchmark (CVPR 2026 · similarity 0.80)
- Contextualized Policy Recovery: Modeling and Interpreting Medical Decisions with Adaptive Imitation Learning (ICML 2024 · similarity 0.79)
- Bridging Vision and Language for Robust Context-Aware Surgical Point Tracking: The VL-SurgPT Dataset and Benchmark (AAAI 2026 · similarity 0.79)
- CholecTrack20: A Multi-Perspective Tracking Dataset for Surgical Tools (CVPR 2025 · similarity 0.78)
- OPERA: Automatic Offline Policy Evaluation with Re-weighted Aggregates of Multiple Estimators (NeurIPS 2024 · similarity 0.78)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.