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Found 32 papers, 11 papers with code
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.
Tracking and Mapping in Medical Computer Vision: A Review
In this review, we provide an update to the field of camera-based tracking and scene mapping in surgery and diagnostics in medical computer vision.
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.
AnyOKP: One-Shot and Instance-Aware Object Keypoint Extraction with Pretrained ViT
Towards flexible object-centric visual perception, we propose a one-shot instance-aware object keypoint (OKP) extraction approach, AnyOKP, which leverages the powerful representation ability of pretrained vision transformer (ViT), and can obtain keypoints on multiple object instances of arbitrary category after learning from a support image.
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.
SemHint-MD: Learning from Noisy Semantic Labels for Self-Supervised Monocular Depth Estimation
Without ground truth supervision, self-supervised depth estimation can be trapped in a local minimum due to the gradient-locality issue of the photometric loss.
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
Image-based Pose Estimation and Shape Reconstruction for Robot Manipulators and Soft, Continuum Robots via Differentiable Rendering
We demonstrate that our method of using geometrical shape primitives can achieve high accuracy in shape reconstruction for a soft continuum robot and pose estimation for a robot manipulator.
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.
Flexible Attention-Based Multi-Policy Fusion for Efficient Deep Reinforcement Learning
In this work, we present Knowledge-Grounded RL (KGRL), an RL paradigm fusing multiple knowledge policies and aiming for human-like efficiency and flexibility.
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.
Motion Planning Transformers: A Motion Planning Framework for Mobile Robots
A popular technique to improve the efficiency of these planners is to restrict search space in the planning domain.
NeRP: Neural Rearrangement Planning for Unknown Objects
We propose NeRP (Neural Rearrangement Planning), a deep learning based approach for multi-step neural object rearrangement planning which works with never-before-seen objects, that is trained on simulation data, and generalizes to the real world.
Data-driven Actuator Selection for Artificial Muscle-Powered Robots
Even though artificial muscles have gained popularity due to their compliant, flexible, and compact properties, there currently does not exist an easy way of making informed decisions on the appropriate actuation strategy when designing a muscle-powered robot; thus limiting the transition of such technologies into broader applications.
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
MPC-MPNet: Model-Predictive Motion Planning Networks for Fast, Near-Optimal Planning under Kinodynamic Constraints
Kinodynamic Motion Planning (KMP) is to find a robot motion subject to concurrent kinematics and dynamics constraints.
Constrained Motion Planning Networks X
However, few solutions to constrained motion planning are available, and those that exist struggle with high computational time complexity in finding a path solution on the manifolds.
Neural Manipulation Planning on Constraint Manifolds
The presence of task constraints imposes a significant challenge to motion planning.
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.
Motion Planning Networks: Bridging the Gap Between Learning-based and Classical Motion Planners
We validate MPNet against gold-standard and state-of-the-art planning methods in a variety of problems from 2D to 7D robot configuration spaces in challenging and cluttered environments, with results showing significant and consistently stronger performance metrics, and motivating neural planning in general as a modern strategy for solving motion planning problems efficiently.
Composing Task-Agnostic Policies with Deep Reinforcement Learning
The composition of elementary behaviors to solve challenging transfer learning problems is one of the key elements in building intelligent machines.
Neural Path Planning: Fixed Time, Near-Optimal Path Generation via Oracle Imitation
In this work, we introduce a novel way of producing fast and optimal motion plans for static environments by using a stepping neural network approach, called OracleNet.
An Open-Source 7-Axis, Robotic Platform to Enable Dexterous Procedures within CT Scanners
This paper describes the design, manufacture, and performance of a highly dexterous, low-profile, 7 Degree-of-Freedom (DOF) robotic arm for CT-guided percutaneous needle biopsy.
Robotics
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
Deeply Informed Neural Sampling for Robot Motion Planning
In this paper, we present a neural network-based adaptive sampler for motion planning called Deep Sampling-based Motion Planner (DeepSMP).
Adversarial Imitation via Variational Inverse Reinforcement Learning
We consider a problem of learning the reward and policy from expert examples under unknown dynamics.
Motion Planning Networks
Fast and efficient motion planning algorithms are crucial for many state-of-the-art robotics applications such as self-driving cars.
