SurRoL: An Open-source Reinforcement Learning Centered and dVRK Compatible Platform for Surgical Robot Learning

Xu, Jiaqi; Li, Bin; Lu, Bo; Liu, Yunhui; Dou, Qi; Heng, Pheng‐Ann

doi:10.1109/iros51168.2021.9635867

Cited by 48 publications

(21 citation statements)

References 24 publications

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“…Such metrics are e.g., number of errors , quality of the outcome , and success rate . Success rate is probably the most universal and easy to measure, thus utilized frequently in surgical subtask automation [ 15 , 19 , 27 , 29 , 31 , 54 , 55 , 57 ]. McKinley et al [ 57 ] evaluated autonomous tumor resection by end-to-end success rate , while Hwang et al [ 27 ] defined it for the peg transfer training exercise as the percentile value of the ratio of Success/Attempts for each individual transfer.…”

Section: Performance Metricsmentioning

confidence: 99%

“…In the most recent years, the automation of simple surgical training exercises on rigid [ 22 , 23 , 24 , 25 , 26 , 27 , 28 ] or deformable [ 29 , 30 ] phantoms tends to receive increasing attention. Among all the training exercises, the automation of different versions of peg transfer is presented in the most significant number of studies [ 22 , 23 , 24 , 25 , 26 , 27 , 28 , 31 ], probably due to its simplicity, enabling to elaborate the basic principles and best algorithms for automation.…”

Section: Introductionmentioning

confidence: 99%

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Performance and Capability Assessment in Surgical Subtask Automation

Nagy

Haidegger

2022

Sensors

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Robot-Assisted Minimally Invasive Surgery (RAMIS) has reshaped the standard clinical practice during the past two decades. Many believe that the next big step in the advancement of RAMIS will be partial autonomy, which may reduce the fatigue and the cognitive load on the surgeon by performing the monotonous, time-consuming subtasks of the surgical procedure autonomously. Although serious research efforts are paid to this area worldwide, standard evaluation methods, metrics, or benchmarking techniques are still not formed. This article aims to fill the void in the research domain of surgical subtask automation by proposing standard methodologies for performance evaluation. For that purpose, a novel characterization model is presented for surgical automation. The current metrics for performance evaluation and comparison are overviewed and analyzed, and a workflow model is presented that can help researchers to identify and apply their choice of metrics. Existing systems and setups that serve or could serve as benchmarks are also introduced and the need for standard benchmarks in the field is articulated. Finally, the matter of Human–Machine Interface (HMI) quality, robustness, and the related legal and ethical issues are presented.

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Section: Performance Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Performance and Capability Assessment in Surgical Subtask Automation

Nagy

Haidegger

2022

Sensors

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“…Especially for surgery robot tasks, data collecting is expensive and even impossible in many cases. Therefore, open-sourced RL environments for surgical robotics, such as dVRL [183] and SurRoL [184] , have been developed to encourage sim-to-real transfer learning research for robotic surgery.…”

Section: Rl For Autonomous Ramsmentioning

confidence: 99%

From Teleoperation to Autonomous Robot-assisted Microsurgery: A Survey

Zhang

Fang

et al. 2022

Mach. Intell. Res.

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Robot-assisted microsurgery (RAMS) has many benefits compared to traditional microsurgery. Microsurgical platforms with advanced control strategies, high-quality micro-imaging modalities and micro-sensing systems are worth developing to further enhance the clinical outcomes of RAMS. Within only a few decades, microsurgical robotics has evolved into a rapidly developing research field with increasing attention all over the world. Despite the appreciated benefits, significant challenges remain to be solved. In this review paper, the emerging concepts and achievements of RAMS will be presented. We introduce the development tendency of RAMS from teleoperation to autonomous systems. We highlight the upcoming new research opportunities that require joint efforts from both clinicians and engineers to pursue further outcomes for RAMS in years to come.

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“…Recently, Reinforcement Learning (RL) [1] has been put to significant uses such as robotic table tennis [2], surgical robot planning [3], rapid motion planning in bimanual regrasping for suture needles [4] and Aquatic Navigation [5]. Each of these applications make use of RL as an encouraging substitute to automating manual effort.…”

Section: Introductionmentioning

confidence: 99%

GA-DRL: Genetic Algorithm-Based Function Optimizer in Deep Reinforcement Learning for Robotic Manipulation Tasks

Sehgal¹,

Ward²,

La³

et al. 2022

Preprint

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Reinforcement learning (RL) enables agents to make a decision based on a reward function. However, in the process of learning, the choice of values for learning algorithm parameters can significantly impact the overall learning process. In this paper, we proposed a Genetic Algorithm-based Deep Deterministic Policy Gradient and Hindsight Experience Replay method (called GA-DRL) to find near-optimal values of learning parameters. We used the proposed GA-DRL method on fetch-reach, slide, push, pick and place, and door opening in robotic manipulation tasks. With some modifications, our proposed GA-DRL method was also applied to the auboreach environment. Our experimental evaluation shows that our method leads to significantly better performance, faster than the original algorithm. Also, we provide evidence that GA-DRL performs better than the existing methods.

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SurRoL: An Open-source Reinforcement Learning Centered and dVRK Compatible Platform for Surgical Robot Learning

Cited by 48 publications

References 24 publications

Performance and Capability Assessment in Surgical Subtask Automation

Performance and Capability Assessment in Surgical Subtask Automation

From Teleoperation to Autonomous Robot-assisted Microsurgery: A Survey

GA-DRL: Genetic Algorithm-Based Function Optimizer in Deep Reinforcement Learning for Robotic Manipulation Tasks

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