Safety Augmented Value Estimation From Demonstrations (SAVED): Safe Deep Model-Based RL for Sparse Cost Robotic Tasks

Thananjeyan, Brijen; Rosolia, Ugo; Li, Felix; McAllister, Rowan; Gonzalez, Joseph E.; Levine, Sergey; Borrelli, Francesco; Goldberg, Ken

doi:10.1109/lra.2020.2976272

Cited by 73 publications

(71 citation statements)

References 15 publications

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“…Six common surgical tasks appear to be widely investigated for automation. For the suturing task, including the works related to knot tying and needle insertion, we reported the following: [16], [70], [85], [88], [89], [161], [178], [195]- [197], [203], [243], and [246]. The pick, transfer, and place task was mainly characterized by experiments relying on pegs and rings from the fundamentals of the laparoscopic surgery training paradigm [54], [69], [91], [96], [102], [151]- [153], [163], [264] or new surgical tools [177].…”

Section: Instrument Controlmentioning

confidence: 99%

Accelerating Surgical Robotics Research: A Review of 10 Years With the da Vinci Research Kit

D'Ettorre

Mariani

Stilli

et al. 2021

IEEE Robot. Automat. Mag.

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This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.R obotic-assisted surgery is now well established in clinical practice and has become the gold-standard clinical treatment option for several clinical indications. The field of robotic-assisted surgery is expected to grow substantially in the next decade, with a range of new robotic devices emerging to address unmet clinical needs across different specialties. A vibrant surgical robotics research community is pivotal for conceptualizing such new systems as well as for developing and training the engineers and scientists to translate them into practice. The da Vinci Research Kit (dVRK), an academic and industry collaborative effort to repurpose decommissioned da Vinci surgical systems [Intuitive Surgical Inc. (ISI), California, USA] as a research platform for surgical robotics research, has been a key initiative for addressing a barrier to entry for new research groups in surgical robotics. In this article, we present an extensive review of the publications that have been facilitated by the dVRK over the past decade. We classify research efforts into different categories and outline some of the major challenges and needs for the robotics community to maintain and build upon this initiative.

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Section: Instrument Controlmentioning

confidence: 99%

Accelerating Surgical Robotics Research: A Review of 10 Years With the da Vinci Research Kit

D'Ettorre

Mariani

Stilli

et al. 2021

IEEE Robot. Automat. Mag.

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show abstract

“…Automation of surgical subtasks is an active area of research with a rich history. Prior literature has studied automation of tasks related to surgical cutting [19,23,34], debridement [16,23], hemostasis [26], peg transfer [12,13,24], and suturing [7,9,28,[31][32][33]. While automated suturing has been studied in prior work [28,31], suturing without modifications such as painted fiducial markers is an open research problem.…”

Section: A Automation In Surgical Roboticsmentioning

confidence: 99%

Learning to Localize, Grasp, and Hand Over Unmodified Surgical Needles

Wilcox¹,

Kerr²,

Thananjeyan³

et al. 2021

Preprint

Self Cite

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Robotic Surgical Assistants (RSAs) are commonly used to perform minimally invasive surgeries by expert surgeons. However, long procedures filled with tedious and repetitive tasks such as suturing can lead to surgeon fatigue, motivating the automation of suturing. As visual tracking of a thin reflective needle is extremely challenging, prior work has modified the needle with nonreflective contrasting paint. As a step towards automation of a suturing subtask without modifying the needle, we propose HOUSTON: Handoff of Unmodified, Surgical, Tool-Obstructed Needles, a problem and algorithm that uses a learned active sensing policy with a stereo camera to localize and align the needle into a visible and accessible pose for the other arm. To compensate for robot positioning and needle perception errors, the algorithm then executes a high-precision grasping motion that uses multiple cameras. In physical experiments using the da Vinci Research Kit (dVRK), HOUSTON successfully passes unmodified surgical needles with a success rate of 96.7% and is able to perform handover sequentially between the arms 32.4 times on average before failure. On needles unseen in training, HOUSTON achieves a success rate of 75 − 92.9%. To our knowledge, this work is the first to study handover of unmodified surgical needles. See https://tinyurl.com/houston-surgery for additional materials.

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“…Then using another value function trained network and an evolutionary algorithm, the sequence of actions is optimized and the first action of the optimized sequence is executed. Thananjeyan et al [55] implements deep MPC over learned dynamics in a constrained environment. To ensure exploration without violating the constraints, generated trajectories are conditioned to where a plan exists for navigating back to a safe set.…”

Section: Related Workmentioning

confidence: 99%