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Found 13 papers, 5 papers with code
The State of Robot Motion Generation
This paper reviews the large spectrum of methods for generating robot motion proposed over the 50 years of robotics research culminating in recent developments.
A Survey on the Integration of Machine Learning with Sampling-based Motion Planning
It also discusses how machine learning has been used to provide data-driven models of robots, which can then be used by a SBMP.
Efficient and High-quality Prehensile Rearrangement in Cluttered and Confined Spaces
The new monotone solver is integrated with a global planner to solve non-monotone instances with high-quality solutions fast.
Uniform Object Rearrangement: From Complete Monotone Primitives to Efficient Non-Monotone Informed Search
DFSDP is extended to solve single-buffer, non-monotone instances, given a choice of an object and a buffer.
Safe and Effective Picking Paths in Clutter given Discrete Distributions of Object Poses
This work proposes first a perception process for 6D pose estimation, which returns a discrete distribution of object poses in a scene.
se(3)-TrackNet: Data-driven 6D Pose Tracking by Calibrating Image Residuals in Synthetic Domains
Tracking the 6D pose of objects in video sequences is important for robot manipulation.
Ranked #5 on
6D Pose Estimation
on YCB-Video
Robust, Occlusion-aware Pose Estimation for Objects Grasped by Adaptive Hands
The hand's point cloud is pruned and robust global registration is performed to generate object pose hypotheses, which are clustered.
6D Pose Estimation using RGB
6D Pose Estimation using RGBD
+4
Efficient Model Identification for Tensegrity Locomotion
This paper aims to identify in a practical manner unknown physical parameters, such as mechanical models of actuated robot links, which are critical in dynamical robotic tasks.
Fast Model Identification via Physics Engines for Data-Efficient Policy Search
This paper presents a method for identifying mechanical parameters of robots or objects, such as their mass and friction coefficients.
Improving 6D Pose Estimation of Objects in Clutter via Physics-aware Monte Carlo Tree Search
Experimental results indicate that this process is able to quickly identify in cluttered scenes physically-consistent object poses that are significantly closer to ground truth compared to poses found by point cloud registration methods.
A Self-supervised Learning System for Object Detection using Physics Simulation and Multi-view Pose Estimation
The models are placed in physically realistic poses with respect to their environment to generate a labeled synthetic dataset.
Analysis and Observations from the First Amazon Picking Challenge
This paper presents a overview of the inaugural Amazon Picking Challenge along with a summary of a survey conducted among the 26 participating teams.
Robotics
A Dataset for Improved RGBD-based Object Detection and Pose Estimation for Warehouse Pick-and-Place
This paper provides a new rich data set for advancing the state-of-the-art in RGBD- based 3D object pose estimation, which is focused on the challenges that arise when solving warehouse pick- and-place tasks.
