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Sim-to-Real 6D Object Pose Estimation via Iterative Self-training for Robotic Bin Picking
[article]
2022
arXiv
pre-print
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In this paper, we propose an iterative self-training framework for sim-to-real 6D object pose estimation to facilitate cost-effective robotic grasping. ...
Our method is also able to improve robotic bin-picking success by 19.54%, demonstrating the potential of iterative sim-to-real solutions for robotic applications. ...
The work was supported by the Hong Kong Centre for Logistics Robotics. ...
arXiv:2204.07049v2
fatcat:ex5fmjaphnh6bicou2bndkxkbi
DemoGrasp: Few-Shot Learning for Robotic Grasping with Human Demonstration
[article]
2021
arXiv
pre-print
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To this end, most approaches either compute the full 6D pose for the object of interest or learn to predict a set of grasping points. ...
Finally, we transfer the a-priori knowledge from the relative pose between object and human hand with the estimate of the current object pose in the scene into necessary grasping instructions for the robot ...
Bousmalis, “Sim-to-real via sim-
from rgb images,” in ECCV, 2018. to-sim: Data-efficient robotic grasping via randomized-to-canonical
[28] Y. ...
arXiv:2112.02849v1
fatcat:kpwrzj6r4re6rodinzlrer25si
Physics-based Scene-level Reasoning for Object Pose Estimation in Clutter
[article]
2019
arXiv
pre-print
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Furthermore, confident estimates are used to label online real images from multiple views and re-train the process in a self-learning pipeline. ...
This work proposes an autonomous process for pose estimation that spans from data generation to scene-level reasoning and self-learning. ...
An example of such a setup exists in current day warehouses, where robots are being deployed for tasks such as picking from bins, packing and sorting. ...
arXiv:1806.10457v2
fatcat:hngnpzbfejhqbk6nez6pkeu6m4
You Only Demonstrate Once: Category-Level Manipulation from Single Visual Demonstration
[article]
2022
arXiv
pre-print
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The demonstration is reprojected to a target trajectory tailored to a novel object via the canonical representation. ...
Nevertheless, it often requires expensive real-world data collection and manual specification of semantic keypoints for each object category and task. ...
These methods range from training 6D pose estimators with CAD models to end-to-end reinforcement learning from repeated robot interaction with the task object. ...
arXiv:2201.12716v2
fatcat:qbgchamnlvfvhhu6avtulp6yq4
Visual Foresight Tree for Object Retrieval from Clutter with Nonprehensile Rearrangement
[article]
2021
arXiv
pre-print
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We first show that a deep neural network can be trained to accurately predict the poses of the packed objects when the robot pushes one of them. ...
Object retrieval in dense clutter is an important skill for robots to operate in households and everyday environments effectively. ...
To train GN, we set the reward to be 1 for grasps where the robot successfully picks up only the target object, and 0 otherwise. GN is the reward estimator for states in VFT (in Section V-C). ...
arXiv:2105.02857v2
fatcat:lfmfxh7e4navdnudnzak2oxt6i
2021 Index IEEE Robotics and Automation Letters Vol. 6
2021
IEEE Robotics and Automation Letters
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Departments and other items may also be covered if they have been judged to have archival value. The Author Index contains the primary entry for each item, listed under the first author's name. ...
., +, LRA Oct. 2021 7791-7798 Adversarial Training on Point Clouds for Sim-to-Real 3D Object Detection. ...
., +, LRA Oct. 2021 8363-8370 Adversarial Training on Point Clouds for Sim-to-Real 3D Object Detection. ...
doi:10.1109/lra.2021.3119726
fatcat:lsnerdofvveqhlv7xx7gati2xu
Learning Dexterous Manipulation from Suboptimal Experts
[article]
2021
arXiv
pre-print
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It represents the optimal policy via importance sampling from a learned prior and is well-suited to take advantage of mixed data distributions. ...
reference behaviors to bootstrap a complex manipulation task on a simulated bimanual robot with human-like hands. ...
Self-
supervised sim-to-real adaptation for visual robotic manipulation, 2019.
[21] A. Kurenkov, A. Mandlekar, R. Martin-Martin, S. Savarese, and A. Garg. ...
arXiv:2010.08587v2
fatcat:l3o7m2ht6fakhiuzxbu66trg2i
Table of Contents
2022
IEEE Robotics and Automation Letters
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Chen A Sim-to-Real Object Recognition and Localization Framework for Industrial Robotic Bin Picking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .X. Li, R. ...
Wensing iCaps: Iterative Category-Level Object Pose and Shape Estimation . . . . . X. Deng, J. Geng, T. Bretl, Y. Xiang, and D. ...
doi:10.1109/lra.2022.3165102
fatcat:enjzebowe5hn7hsfwklc7nieuy
2020 Index IEEE Robotics and Automation Letters Vol. 5
2020
IEEE Robotics and Automation Letters
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., +, LRA Oct. 2020
6686-6693
A Bin-Picking Benchmark for Systematic Evaluation of Robotic Pick-and-
Place Systems. ...
Sim-To-Real Transfer Learning Approach for Tracking Multi-DOF Ankle Motions Using Soft Strain Sensors. ...
doi:10.1109/lra.2020.3032821
fatcat:qrnouccm7jb47ipq6w3erf3cja
Interactive Open-Ended Learning for 3D Object Recognition
[article]
2019
arXiv
pre-print
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In this context, "open-ended" implies that the set of categories to be learned is not known in advance, and the training instances are extracted from actual experiences of a robot, and thus become gradually ...
In particular, this architecture provides perception capabilities that will allow robots to incrementally learn object categories from the set of accumulated experiences and reason about how to perform ...
In this step, the robot captures a point cloud of the scene and computes a list of object
hypotheses containing both objects’ 6D pose and recognized label (i.e., object recognition and
pose estimation ...
arXiv:1912.09539v1
fatcat:aksfik7earfafpe7n5amsehc2e
Solving Rubik's Cube with a Robot Hand
[article]
2019
arXiv
pre-print
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We demonstrate that models trained only in simulation can be used to solve a manipulation problem of unprecedented complexity on a real robot. ...
The combination of ADR with our custom robot platform allows us to solve a Rubik's cube with a humanoid robot hand, which involves both control and state estimation problems. ...
Acknowledgements We would like to thank Shadow Robot Company Ltd. for building, maintaining, and improving the Shadow Dexterous Hand, PhaseSpace Inc. for building custom Rubik's cubes and supporting our ...
arXiv:1910.07113v1
fatcat:pqy6tcdd6jfuximspvl6nia2su
Strategy for an Autonomous Behavior that Guarantees a Qualitative Fine Adjustment at the Target Pose of a Collaborating Mobile Robot
2021
Zenodo
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This thesis introduces a strategy that leverages a pose graph-based localization approach to reduce positioning errors at target poses of collaborating mobile robots. ...
As a basis for this strategy, an extensible software architecture for socially acceptable navigation of mobile robots is designed and implemented using ROS components. ...
It al-
lows clients to subscribe to topics using sim-
ple JSON or BSON objects. ...
doi:10.5281/zenodo.4742013
fatcat:637baqamlffkfda5cvvxzegyuy
Learning Neural Textual Representations for Citation Recommendation
2021
2020 25th International Conference on Pattern Recognition (ICPR)
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366
TSDM: Tracking by SiamRPN++ with a Depth-Refiner and a Mask-
Generator
DAY 3 -Jan 14, 2021
Feng, Hangtao; Zhang, Lu; Yang,
Xu; Liu, Zhiyong
369
MixedFusion: 6D Object Pose Estimation from ...
DAY 3 -Jan 14, 2021
Guo, Haifeng; Lu, Tong; Wu, Yirui
1529
Dynamic Low-Light Image Enhancement for Object Detection Via
End-To-End Training
DAY 3 -Jan 14, 2021
Zhu, Yongpei; Zhou, Zicong; Liao ...
doi:10.1109/icpr48806.2021.9412725
fatcat:3vge2tpd2zf7jcv5btcixnaikm
Cost-Efficient Global Robot Navigation in Rugged Off-Road Terrain
2011
Künstliche Intelligenz
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Abstract This thesis addresses the problem of finding a global robot navigation strategy for rugged off-road terrain which is robust against inaccurate self-localization and scalable to large environments ...
Thanks to Norbert Schmitz for maintaining and fixing many things in our software framework, Daniel Schmidt for putting up with the chores of teaching and undergraduate education, Jochen Hirth for his work ...
The mathematical technique to convert between a 6D robot pose (x, y, z, roll, pitch, yaw) specified in the W ORLD coordinate system and the corresponding transformation matrix M W ORLD ROBOT is documented ...
doi:10.1007/s13218-011-0088-9
fatcat:u4ssainmcvc3jmqdon6gpjg674
Scalable, physics-aware 6D pose estimation for robot manipulation
2020
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Then it self-improves over time by acquiring and labeling real-world images via a search-based pose estimation process. ...
Robot Manipulation often depend on some form of pose estimation to represent the state of the world and allow decision making both at the task-level and for motion or grasp planning. ...
Self-Learning via Multi-view Pose Estimation Given access to an object detector and a pose estimation process trained with the physics-based simulator, the self-learning pipeline labels real-world images ...
doi:10.7282/t3-s5fk-ht60
fatcat:lndswo56sfhdvkmsmuigsvr4hi
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