Sim-to-Real 6D Object Pose Estimation via Iterative Self-training for Robotic Bin-picking

• URL: http://arxiv.org/abs/2204.07049v1
• Date: Thu, 14 Apr 2022 15:54:01 GMT
• Title: Sim-to-Real 6D Object Pose Estimation via Iterative Self-training for Robotic Bin-picking
• Authors: Kai Chen, Rui Cao, Stephen James, Yichuan Li, Yun-Hui Liu, Pieter Abbeel, and Qi Dou
• Abstract summary: We propose an iterative self-training framework for sim-to-real 6D object pose estimation to facilitate cost-effective robotic grasping. We establish a photo-realistic simulator to synthesize abundant virtual data, and use this to train an initial pose estimation network. This network then takes the role of a teacher model, which generates pose predictions for unlabeled real data.
• Score: 98.5984733963713
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we propose an iterative self-training framework for sim-to-real 6D object pose estimation to facilitate cost-effective robotic grasping. Given a bin-picking scenario, we establish a photo-realistic simulator to synthesize abundant virtual data, and use this to train an initial pose estimation network. This network then takes the role of a teacher model, which generates pose predictions for unlabeled real data. With these predictions, we further design a comprehensive adaptive selection scheme to distinguish reliable results, and leverage them as pseudo labels to update a student model for pose estimation on real data. To continuously improve the quality of pseudo labels, we iterate the above steps by taking the trained student model as a new teacher and re-label real data using the refined teacher model. We evaluate our method on a public benchmark and our newly-released dataset, achieving an ADD(-S) improvement of 11.49% and 22.62% respectively. 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.

Related papers

• Exploring the Effectiveness of Dataset Synthesis: An application of Apple Detection in Orchards [68.95806641664713]
  We explore the usability of Stable Diffusion 2.1-base for generating synthetic datasets of apple trees for object detection. We train a YOLOv5m object detection model to predict apples in a real-world apple detection dataset. Results demonstrate that the model trained on generated data is slightly underperforming compared to a baseline model trained on real-world images.
  arXiv  Detail & Related papers  (2023-06-20T09:46:01Z)
• Robust Category-Level 3D Pose Estimation from Synthetic Data [17.247607850702558]
  We introduce SyntheticP3D, a new synthetic dataset for object pose estimation generated from CAD models. We propose a novel approach (CC3D) for training neural mesh models that perform pose estimation via inverse rendering.
  arXiv  Detail & Related papers  (2023-05-25T14:56:03Z)
• Markerless Camera-to-Robot Pose Estimation via Self-supervised Sim-to-Real Transfer [26.21320177775571]
  We propose an end-to-end pose estimation framework that is capable of online camera-to-robot calibration and a self-supervised training method. Our framework combines deep learning and geometric vision for solving the robot pose, and the pipeline is fully differentiable.
  arXiv  Detail & Related papers  (2023-02-28T05:55:42Z)
• Towards Precise Model-free Robotic Grasping with Sim-to-Real Transfer Learning [11.470950882435927]
  We present an end-to-end robotic grasping network with a grasp. In physical robotic experiments, our grasping framework grasped single known objects and novel complex-shaped household objects with a success rate of 90.91%. The proposed grasping framework outperformed two state-of-the-art methods in both known and unknown object robotic grasping.
  arXiv  Detail & Related papers  (2023-01-28T16:57:19Z)
• Self-Distillation for Further Pre-training of Transformers [83.84227016847096]
  We propose self-distillation as a regularization for a further pre-training stage. We empirically validate the efficacy of self-distillation on a variety of benchmark datasets for image and text classification tasks.
  arXiv  Detail & Related papers  (2022-09-30T02:25:12Z)
• Real-to-Sim: Predicting Residual Errors of Robotic Systems with Sparse Data using a Learning-based Unscented Kalman Filter [65.93205328894608]
  We learn the residual errors between a dynamic and/or simulator model and the real robot. We show that with the learned residual errors, we can further close the reality gap between dynamic models, simulations, and actual hardware.
  arXiv  Detail & Related papers  (2022-09-07T15:15:12Z)
• Object Detection Using Sim2Real Domain Randomization for Robotic Applications [0.0]
  We propose a sim2real transfer learning method based on domain randomization for object detection. A state-of-the-art convolutional neural network, YOLOv4, is trained to detect the different types of industrial objects. Our solution matches industrial needs as it can reliably differentiate similar classes of objects by using only 1 real image for training.
  arXiv  Detail & Related papers  (2022-08-08T14:16:45Z)
• Sim2Real Instance-Level Style Transfer for 6D Pose Estimation [0.4893345190925177]
  We introduce a simulation to reality (sim2real) instance-level style transfer for 6D pose estimation network training. Our approach transfers the style of target objects individually, from synthetic to real, without human intervention.
  arXiv  Detail & Related papers  (2022-03-03T23:46:47Z)
• SimAug: Learning Robust Representations from Simulation for Trajectory Prediction [78.91518036949918]
  We propose a novel approach to learn robust representation through augmenting the simulation training data. We show that SimAug achieves promising results on three real-world benchmarks using zero real training data.
  arXiv  Detail & Related papers  (2020-04-04T21:22:01Z)
• CPS++: Improving Class-level 6D Pose and Shape Estimation From Monocular Images With Self-Supervised Learning [74.53664270194643]
  Modern monocular 6D pose estimation methods can only cope with a handful of object instances. We propose a novel method for class-level monocular 6D pose estimation, coupled with metric shape retrieval. We experimentally demonstrate that we can retrieve precise 6D poses and metric shapes from a single RGB image.
  arXiv  Detail & Related papers  (2020-03-12T15:28:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.