mlsanityData to 7 Oct 2026

ReorientBot: Learning Object Reorientation for Specific-Posed Placement

arXiv 2202.11092 ↗First posted 22 Feb 2022cs.RO cs.AI cs.CVcited by 10

Not on the map. Why?

Abstract (arXiv)

Robots need the capability of placing objects in arbitrary, specific poses to rearrange the world and achieve various valuable tasks. Object reorientation plays a crucial role in this as objects may not initially be oriented such that the robot can grasp and then immediately place them in a specific goal pose. In this work, we present a vision-based manipulation system, ReorientBot, which consists of 1) visual scene understanding with pose estimation and volumetric reconstruction using an onboard RGB-D camera; 2) learned waypoint selection for successful and efficient motion generation for reorientation; 3) traditional motion planning to generate a collision-free trajectory from the selected waypoints. We evaluate our method using the YCB objects in both simulation and the real world, achieving 93% overall success, 81% improvement in success rate, and 22% improvement in execution time compared to a heuristic approach. We demonstrate extended multi-object rearrangement showing the general capability of the system.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 23 of its 37 references that are in the map. First posted on arXiv 22 Feb 2022.

Builds on

23 of 37 references.

References · 23

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  12. 2019kPAM: KeyPoint Affordances for Category-Level Robotic Manipulationcited by 152
11 more
  1. 2017Robotic Pick-and-Place of Novel Objects in Clutter with Multi-Affordance Grasping and Cross-Domain Image Matchingcited by 124
  2. 2021Coarse-to-Fine Q-attention: Efficient Learning for Visual Robotic Manipulation via Discretisationcited by 120
  3. 2017Intel RealSense Stereoscopic Depth Camerascited by 82
  4. 2016Multi-view Self-supervised Deep Learning for 6D Pose Estimation in the Amazon Picking Challengecited by 70
  5. 2020MoreFusion: Multi-object Reasoning for 6D Pose Estimation from Volumetric Fusioncited by 46
  6. 2020Task-driven Perception and Manipulation for Constrained Placement of Unknown Objectscited by 11
  7. 2021Learning to Regrasp by Learning to Placecited by 11
  8. 2017Pick and Place Without Geometric Object Modelscited by 9
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  11. 2022SafePicking: Learning Safe Object Extraction via Object-Level Mappingcited by 3

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Cited by year

Citing papers per year: 2022: 3; 2023: 5; 2024: 2.

10 papers.

  1. 2022Real-World Robot Learning with Masked Visual Pre-trainingcited by 241
  2. 2023Shelving, Stacking, Hanging: Relational Pose Diffusion for Multi-modal Rearrangementcited by 29
  3. 2022Sim-to-Real 6D Object Pose Estimation via Iterative Self-training for Robotic Bin Pickingcited by 20
  4. 2023ReorientDiff: Diffusion Model based Reorientation for Object Manipulationcited by 19
  5. 2023CabiNet: Scaling Neural Collision Detection for Object Rearrangement with Procedural Scene Generationcited by 17
  6. 2023Dream2Real: Zero-Shot 3D Object Rearrangement with Vision-Language Modelscited by 16
  7. 2022Coarse-to-fine Q-attention with Tree Expansioncited by 9
  8. 2023You Only Look at One: Category-Level Object Representations for Pose Estimation From a Single Examplecited by 8
  9. 2024Grasp, See, and Place: Efficient Unknown Object Rearrangement with Policy Structure Priorcited by 5
  10. 2024Synthetica: Large Scale Synthetic Data for Robot Perceptioncited by 3