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Found 19 papers, 4 papers with code
KineDepth: Utilizing Robot Kinematics for Online Metric Depth Estimation
Depth perception is essential for a robot's spatial and geometric understanding of its environment, with many tasks traditionally relying on hardware-based depth sensors like RGB-D or stereo cameras.
HemoSet: The First Blood Segmentation Dataset for Automation of Hemostasis Management
The first step in automation of hemostasis management is detection of blood in the surgical field.
Robust Surgical Tool Tracking with Pixel-based Probabilities for Projected Geometric Primitives
Uncertainties in mechanical systems as well as camera calibration create errors in this coordinate frame transformation.
SuPerPM: A Large Deformation-Robust Surgical Perception Framework Based on Deep Point Matching Learned from Physical Constrained Simulation Data
Thus, for tuning the learning model, we gather endoscopic data of soft tissue being manipulated by a surgical robot and then establish correspondences between point clouds at different time points to serve as ground truth.
Investigating Low Data, Confidence Aware Image Prediction on Smooth Repetitive Videos using Gaussian Processes
Additionally these methods are reliant on the availability of large training datasets to converge to useful solutions.
Markerless Camera-to-Robot Pose Estimation via Self-supervised Sim-to-Real Transfer
Solving the camera-to-robot pose is a fundamental requirement for vision-based robot control, and is a process that takes considerable effort and cares to make accurate.
Ranked #2 on
Robot Pose Estimation
on DREAM-dataset
Semantic-SuPer: A Semantic-aware Surgical Perception Framework for Endoscopic Tissue Identification, Reconstruction, and Tracking
In this paper, we present a novel, comprehensive surgical perception framework, Semantic-SuPer, that integrates geometric and semantic information to facilitate data association, 3D reconstruction, and tracking of endoscopic scenes, benefiting downstream tasks like surgical navigation.
Real-Time Constrained 6D Object-Pose Tracking of An In-Hand Suture Needle for Minimally Invasive Robotic Surgery
In this work, we consider feasible grasping constraints when tracking the 6D pose of an in-hand suture needle.
Image Based Reconstruction of Liquids from 2D Surface Detections
In this work, we present a solution to the challenging problem of reconstructing liquids from image data.
Towards Non-Parametric Models for Confidence Aware Video Prediction on Smooth Dynamics
In this paper we propose a non-parametric method using Gaussian Process models to propagate probability distributions over sequentially predicted images for confidence aware video prediction with little training.
Markerless Suture Needle 6D Pose Tracking with Robust Uncertainty Estimation for Autonomous Minimally Invasive Robotic Surgery
Previous approaches in autonomous suturing often relied on fiducial markers rather than markerless detection schemes for localizing a suture needle due to the inconsistency of markerless detections.
Model-Predictive Control of Blood Suction for Surgical Hemostasis using Differentiable Fluid Simulations
The fully differentiable fluid dynamics is integrated with a novel suction model for effective model predictive control of the tool.
Robotics
Pose Estimation for Robot Manipulators via Keypoint Optimization and Sim-to-Real Transfer
The physical world experiments show how the proposed method can be applied to the wide-breadth of robotic applications that require visual feedback, such as camera-to-robot calibration, robotic tool tracking, and end-effector pose estimation.
Space of Reasons and Mathematical Model
The conceptual background of representation in models is discussed and in the end I propose how implications of propositional logic and conceptual determinations can be represented in a model of a neural network.
Inferences and Modal Vocabulary
Material inferences are based on modal vocabulary, which enriches the logical expressivity of the inferential relations.
Logic, Language, and Calculus
The difference between object-language and metalanguage is crucial for logical analysis, but has yet not been examined for the field of computer science.
SuPer Deep: A Surgical Perception Framework for Robotic Tissue Manipulation using Deep Learning for Feature Extraction
Robotic automation in surgery requires precise tracking of surgical tools and mapping of deformable tissue.
SuPer: A Surgical Perception Framework for Endoscopic Tissue Manipulation with Surgical Robotics
In this work, we propose a novel surgical perception framework, SuPer, for surgical robotic control.
Open-Sourced Reinforcement Learning Environments for Surgical Robotics
Reinforcement Learning (RL) is a machine learning framework for artificially intelligent systems to solve a variety of complex problems.
Robotics
