Robotic Constrained Imitation Learning for the Peg Transfer Task in Fundamentals of Laparoscopic Surgery
(2405.03440)Abstract
In this study, we present an implementation strategy for a robot that performs peg transfer tasks in Fundamentals of Laparoscopic Surgery (FLS) via imitation learning, aimed at the development of an autonomous robot for laparoscopic surgery. Robotic laparoscopic surgery presents two main challenges: (1) the need to manipulate forceps using ports established on the body surface as fulcrums, and (2) difficulty in perceiving depth information when working with a monocular camera that displays its images on a monitor. Especially, regarding issue (2), most prior research has assumed the availability of depth images or models of a target to be operated on. Therefore, in this study, we achieve more accurate imitation learning with only monocular images by extracting motion constraints from one exemplary motion of skilled operators, collecting data based on these constraints, and conducting imitation learning based on the collected data. We implemented an overall system using two Franka Emika Panda Robot Arms and validated its effectiveness.
Overview
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The paper investigates the application of robotic laparoscopic surgery using a method called imitation learning to address the challenges of instrument manipulation and depth perception.
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A constrained imitation learning approach is developed to allow robots to mimic expert human actions more precisely, using a training setup that involves Franka Emika Panda Robot Arms and a touch-based control system.
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The results show that robots can perform the peg transfer task with high accuracy and suggest possibilities for future improvements in automated surgical training and adaptability to different surgical scenarios.
Exploring Robotic Laparoscopic Surgery through Imitation Learning
The Challenge of Robotic Surgery
Robotic laparoscopic surgery allows for minimally invasive procedures, which typically result in reduced recovery times and less scarring compared to traditional surgery. However, controlling the robotic instruments presents unique challenges:
- Manipulating Instruments: The instruments, such as forceps, must be manipulated accurately using ports on the body as pivots, without placing undue stress on these entry points.
- Depth Perception Difficulties: Surgeons typically rely on 2D images from a monocular camera, making it hard to gauge depth accurately.
Implementation Strategy Explored
The study explores a robot system designed to perform a peg transfer task—a fundamental exercise in laparoscopic training. The key component here is using imitation learning, where the robot learns to mimic expert human actions.
System Setup and Components:
- Robot Arms: Two Franka Emika Panda Robot Arms are used.
- FLS Box Kit: A training kit that simulates a simplified surgical environment.
- Control Devices: Touch Haptic Devices enable experts to demonstrate the task manually, providing inputs to the robot.
Constrained Imitation Learning Approach
A novel approach called constrained imitation learning is introduced to overcome the difficulty in perceiving depth from monocular images.
Steps Involved in Learning:
- Extract Constraints: Motion constraints are derived from a meticulously performed exemplary demonstration by an expert.
- Data Collection with Constraints: Using the derived constraints, data is collected as experts perform the task under guided conditions.
- Train the Model: An imitation learning model is trained on this constrained data, aiming to replicate the expert's movements.
Results and Evaluation
Key Findings:
- Performance data suggests that setting motion constraints based on an expert’s manual execution improves the robustness and precision of the robot’s task performance.
- The system successfully executed peg transfers autonomously with high accuracy by adhering to the learned constraints.
Practical Implications and Future Work
The ability to accurately replicate expert surgical movements in robotic systems opens new avenues in surgical training and procedures. Future work could focus on:
- Automating Constraint Generation: Enhancing the system’s ability to generate motion constraints autonomously could make it applicable to a broader range of tasks.
- Handling Variability: Incorporating adaptability to handle variations in surgical environments more effectively.
Conclusion
This study offers a significant step forward in robotic laparoscopic surgery, demonstrating a feasible approach to teaching robots complex surgical tasks through imitation of expert movements. The constrained imitation learning approach not only overcomes the limitations posed by monocular vision but also enhances the precision and reliability of robotic movements in a surgical setting.
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