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Found 13 papers, 5 papers with code
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.
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.
Robust 6D Object Pose Estimation with Stochastic Congruent Sets
The pointsets are then matched to congruent sets on the 3D object model to generate pose estimates.
Learning Object Localization and 6D Pose Estimation from Simulation and Weakly Labeled Real Images
We then show that the performance of the detector can be substantially improved by using a small set of weakly annotated real images, where a human provides only a list of objects present in each image without indicating the location of the objects.
Physics-based Scene-level Reasoning for Object Pose Estimation in Clutter
This work proposes an autonomous process for pose estimation that spans from data generation to scene-level reasoning and self-learning.
Scene-level Pose Estimation for Multiple Instances of Densely Packed Objects
To evaluate this method, a dataset of densely packed objects with challenging setups for state-of-the-art approaches is collected.
That and There: Judging the Intent of Pointing Actions with Robotic Arms
This work proposes a set of interpretive principles for how a robotic arm can use pointing actions to communicate task information to people by extending existing models from the related literature.
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
Task-driven Perception and Manipulation for Constrained Placement of Unknown Objects
The effectiveness of the proposed approach is demonstrated by developing a robotic system that picks a previously unseen object from a table-top and places it in a constrained space.
Robotics
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
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.
Data-driven 6D Pose Tracking by Calibrating Image Residuals in Synthetic Domains
This work presents se(3)-TrackNet, a data-driven optimization approach for long term, 6D pose tracking.
ARMBench: An Object-centric Benchmark Dataset for Robotic Manipulation
This paper introduces Amazon Robotic Manipulation Benchmark (ARMBench), a large-scale, object-centric benchmark dataset for robotic manipulation in the context of a warehouse.
