mlsanityData to 7 Oct 2026

Precise Object Placement with Pose Distance Estimations for Different Objects and Grippers

arXiv 2110.00992 ↗First posted 3 Oct 2021cs.RO cs.AI cs.CVcited by 0

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Abstract (arXiv)

This paper introduces a novel approach for the grasping and precise placement of various known rigid objects using multiple grippers within highly cluttered scenes. Using a single depth image of the scene, our method estimates multiple 6D object poses together with an object class, a pose distance for object pose estimation, and a pose distance from a target pose for object placement for each automatically obtained grasp pose with a single forward pass of a neural network. By incorporating model knowledge into the system, our approach has higher success rates for grasping than state-of-the-art model-free approaches. Furthermore, our method chooses grasps that result in significantly more precise object placements than prior model-based work.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 17 of its 34 references that are in the map. First posted on arXiv 3 Oct 2021.

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17 of 34 references.

References · 17

  1. 2016Densely Connected Convolutional Networkscited by 6,691
  2. 2017Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real Worldcited by 1,401
  3. 2018QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulationcited by 730
  4. 2016Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collectioncited by 312
  5. 2017Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metricscited by 259
  6. 2018Sim-to-Real via Sim-to-Sim: Data-efficient Robotic Grasping via Randomized-to-Canonical Adaptation Networkscited by 171
  7. 2017Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Graspingcited by 138
  8. 2020Convolutional Networks with Dense Connectivitycited by 98
  9. 2019Large-scale 6D Object Pose Estimation Dataset for Industrial Bin-Pickingcited by 25
  10. 2020Single Shot 6D Object Pose Estimationcited by 19
  11. 2016Defining the Pose of any 3D Rigid Object and an Associated Distancecited by 17
  12. 2017Dex-Net 3.0: Computing Robust Robot Vacuum Suction Grasp Targets in Point Clouds using a New Analytic Model and Deep Learningcited by 15
5 more
  1. 2018Symmetry Aware Evaluation of 3D Object Detection and Pose Estimation in Scenes of Many Parts in Bulkcited by 12
  2. 2018Real-Time 6D Object Pose Estimation on CPUcited by 7
  3. 2021Transferring Experience from Simulation to the Real World for Precise Pick-And-Place Tasks in Highly Cluttered Scenescited by 6
  4. 2021Investigations on Output Parameterizations of Neural Networks for Single Shot 6D Object Pose Estimationcited by 3
  5. 2021Automatic Grasp Pose Generation for Parallel Jaw Gripperscited by 3

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