148 research outputs found
Deep Reinforcement Learning in Surgical Robotics: Enhancing the Automation Level
Surgical robotics is a rapidly evolving field that is transforming the
landscape of surgeries. Surgical robots have been shown to enhance precision,
minimize invasiveness, and alleviate surgeon fatigue. One promising area of
research in surgical robotics is the use of reinforcement learning to enhance
the automation level. Reinforcement learning is a type of machine learning that
involves training an agent to make decisions based on rewards and punishments.
This literature review aims to comprehensively analyze existing research on
reinforcement learning in surgical robotics. The review identified various
applications of reinforcement learning in surgical robotics, including
pre-operative, intra-body, and percutaneous procedures, listed the typical
studies, and compared their methodologies and results. The findings show that
reinforcement learning has great potential to improve the autonomy of surgical
robots. Reinforcement learning can teach robots to perform complex surgical
tasks, such as suturing and tissue manipulation. It can also improve the
accuracy and precision of surgical robots, making them more effective at
performing surgeries
Automated pick-up of suturing needles for robotic surgical assistance
Robot-assisted laparoscopic prostatectomy (RALP) is a treatment for prostate
cancer that involves complete or nerve sparing removal prostate tissue that
contains cancer. After removal the bladder neck is successively sutured
directly with the urethra. The procedure is called urethrovesical anastomosis
and is one of the most dexterity demanding tasks during RALP. Two suturing
instruments and a pair of needles are used in combination to perform a running
stitch during urethrovesical anastomosis. While robotic instruments provide
enhanced dexterity to perform the anastomosis, it is still highly challenging
and difficult to learn. In this paper, we presents a vision-guided needle
grasping method for automatically grasping the needle that has been inserted
into the patient prior to anastomosis. We aim to automatically grasp the
suturing needle in a position that avoids hand-offs and immediately enables the
start of suturing. The full grasping process can be broken down into: a needle
detection algorithm; an approach phase where the surgical tool moves closer to
the needle based on visual feedback; and a grasping phase through path planning
based on observed surgical practice. Our experimental results show examples of
successful autonomous grasping that has the potential to simplify and decrease
the operational time in RALP by assisting a small component of urethrovesical
anastomosis
DefGoalNet: Contextual Goal Learning from Demonstrations For Deformable Object Manipulation
Shape servoing, a robotic task dedicated to controlling objects to desired
goal shapes, is a promising approach to deformable object manipulation. An
issue arises, however, with the reliance on the specification of a goal shape.
This goal has been obtained either by a laborious domain knowledge engineering
process or by manually manipulating the object into the desired shape and
capturing the goal shape at that specific moment, both of which are impractical
in various robotic applications. In this paper, we solve this problem by
developing a novel neural network DefGoalNet, which learns deformable object
goal shapes directly from a small number of human demonstrations. We
demonstrate our method's effectiveness on various robotic tasks, both in
simulation and on a physical robot. Notably, in the surgical retraction task,
even when trained with as few as 10 demonstrations, our method achieves a
median success percentage of nearly 90%. These results mark a substantial
advancement in enabling shape servoing methods to bring deformable object
manipulation closer to practical, real-world applications.Comment: Submitted to IEEE Conference on Robotics and Automation (ICRA) 2024.
8 pages, 11 figure
Soft Tissue Simulation Environment to Learn Manipulation Tasks in Autonomous Robotic Surgery
Reinforcement Learning (RL) methods have demonstrated promising results for the automation of subtasks in surgical robotic systems. Since many trial and error attempts are required to learn the optimal control policy, RL agent training can be performed in simulation and the learned behavior can be then deployed in real environments. In this work, we introduce an open-source simulation environment providing support for position based dynamics soft bodies simulation and state-of-the-art RL methods. We demonstrate the capabilities of the proposed framework by training an RL agent based on Proximal Policy Optimization in fat tissue manipulation for tumor exposure during a nephrectomy procedure. Leveraging on a preliminary optimization of the simulation parameters, we show that our agent is able to learn the task on a virtual replica of the anatomical environment. The learned behavior is robust to changes in the initial end-effector position. Furthermore, we show that the learned policy can be directly deployed on the da Vinci Research Kit, which is able to execute the trajectories generated by the RL agent. The proposed simulation environment represents an essential component for the development of next-generation robotic systems, where the interaction with the deformable anatomical environment is involved
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