# DARIL: When Imitation Learning outperforms Reinforcement Learning in Surgical Action Planning

arXiv: https://arxiv.org/abs/2507.05011 · HTML: https://mlsanity.com/paper/2507.05011/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Medical and surgical world models, one of 11 directions in World models.
- Builds on 16 of its 20 references that are in the map.
- First posted on arXiv 7 Jul 2025.

## Placed in

- World models / [Medical and surgical world models](https://mlsanity.com/wm/medical-and-surgical-world-models/)
- Offline RL and imitation learning from data / [Safe offline RL](https://mlsanity.com/offlinerl/safe-offline-rl/)

## Builds on (most cited first, 16 of 16)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Dream to Control: Learning Behaviors by Latent Imagination](https://mlsanity.com/paper/1912.01603/) (2019)
- [Mastering Diverse Domains through World Models](https://mlsanity.com/paper/2301.04104/) (2023)
- [GAIA-1: A Generative World Model for Autonomous Driving](https://mlsanity.com/paper/2309.17080/) (2023)
- [TD-MPC2: Scalable, Robust World Models for Continuous Control](https://mlsanity.com/paper/2310.16828/) (2023)
- [Rendezvous: Attention Mechanisms for the Recognition of Surgical Action Triplets in Endoscopic Videos](https://mlsanity.com/paper/2109.03223/) (2021)
- [Recognition of Instrument-Tissue Interactions in Endoscopic Videos via Action Triplets](https://mlsanity.com/paper/2007.05405/) (2020)
- [CholecTriplet2021: A benchmark challenge for surgical action triplet recognition](https://mlsanity.com/paper/2204.04746/) (2022)
- [Data Splits and Metrics for Method Benchmarking on Surgical Action Triplet Datasets](https://mlsanity.com/paper/2204.05235/) (2022)
- [Recognition and Prediction of Surgical Gestures and Trajectories Using Transformer Models in Robot-Assisted Surgery](https://mlsanity.com/paper/2212.01683/) (2022)
- [Multimodal Transformers for Real-Time Surgical Activity Prediction](https://mlsanity.com/paper/2403.06705/) (2024)
- [Autonomous navigation of catheters and guidewires in mechanical thrombectomy using inverse reinforcement learning](https://mlsanity.com/paper/2406.12499/) (2024)
- [Reinforcement Learning for Safe Autonomous Two Device Navigation of Cerebral Vessels in Mechanical Thrombectomy](https://mlsanity.com/paper/2503.24140/) (2025)
- [SWAG: Long-term Surgical Workflow Prediction with Generative-based Anticipation](https://mlsanity.com/paper/2412.18849/) (2024)

## Built on by (most cited first, 1 of 1)

- [SurgGoal: Rethinking Surgical Planning Evaluation via Goal-Satisfiability](https://mlsanity.com/paper/2601.10455/) (2026)

## Abstract (arXiv)

Surgical action planning requires predicting future instrument-verb-target triplets for real-time assistance. While teleoperated robotic surgery provides natural expert demonstrations for imitation learning (IL), reinforcement learning (RL) could potentially discover superior strategies through self-exploration. We present the first comprehensive comparison of IL versus RL for surgical action planning on CholecT50. Our Dual-task Autoregressive Imitation Learning (DARIL) baseline achieves 34.6% action triplet recognition mAP and 33.6% next frame prediction mAP with smooth planning degradation to 29.2% at 10-second horizons. We evaluated three RL variants: world model-based RL, direct video RL, and inverse RL enhancement. Surprisingly, all RL approaches underperformed DARIL--world model RL dropped to 3.1% mAP at 10s while direct video RL achieved only 15.9%. Our analysis reveals that distribution matching on expert-annotated test sets systematically favors IL over potentially valid RL policies that differ from training demonstrations. This challenges assumptions about RL superiority in sequential decision making and provides crucial insights for surgical AI development.