# Autonomous task planning and situation awareness in robotic surgery

arXiv: https://arxiv.org/abs/2004.08911 · HTML: https://mlsanity.com/paper/2004.08911/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2020.

## Built on by (most cited first, 10 of 10)

- [Large AI Models in Health Informatics: Applications, Challenges, and the Future](https://mlsanity.com/paper/2303.11568/) (2023)
- [Value-Informed Skill Chaining for Policy Learning of Long-Horizon Tasks with Surgical Robot](https://mlsanity.com/paper/2307.16503/) (2023)
- [Logic programming for deliberative robotic task planning](https://mlsanity.com/paper/2301.07550/) (2023)
- [Agentic Knowledgeable Self-awareness](https://mlsanity.com/paper/2504.03553/) (2025)
- [Learning Logic Specifications for Soft Policy Guidance in POMCP](https://mlsanity.com/paper/2303.09172/) (2023)
- [SWAG: Long-term Surgical Workflow Prediction with Generative-based Anticipation](https://mlsanity.com/paper/2412.18849/) (2024)
- [Unsupervised identification of surgical robotic actions from small non homogeneous datasets](https://mlsanity.com/paper/2105.08488/) (2021)
- [Reward Learning from Suboptimal Demonstrations with Applications in Surgical Electrocautery](https://mlsanity.com/paper/2404.07185/) (2024)
- [Explainable Interface for Human-Autonomy Teaming: A Survey](https://mlsanity.com/paper/2405.02583/) (2024)
- [Inductive Learning of Robot Task Knowledge from Raw Data and Online Expert Feedback](https://mlsanity.com/paper/2501.07507/) (2025)

## Abstract (arXiv)

The use of robots in minimally invasive surgery has improved the quality of standard surgical procedures. So far, only the automation of simple surgical actions has been investigated by researchers, while the execution of structured tasks requiring reasoning on the environment and the choice among multiple actions is still managed by human surgeons. In this paper, we propose a framework to implement surgical task automation. The framework consists of a task-level reasoning module based on answer set programming, a low-level motion planning module based on dynamic movement primitives, and a situation awareness module. The logic-based reasoning module generates explainable plans and is able to recover from failure conditions, which are identified and explained by the situation awareness module interfacing to a human supervisor, for enhanced safety. Dynamic Movement Primitives allow to replicate the dexterity of surgeons and to adapt to obstacles and changes in the environment. The framework is validated on different versions of the standard surgical training peg-and-ring task.