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

arXiv: https://arxiv.org/abs/2110.00992 · HTML: https://mlsanity.com/paper/2110.00992/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 34 references that are in the map.
- First posted on arXiv 3 Oct 2021.

## Builds on (most cited first, 17 of 17)

- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation](https://mlsanity.com/paper/1806.10293/) (2018)
- [Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection](https://mlsanity.com/paper/1603.02199/) (2016)
- [Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics](https://mlsanity.com/paper/1703.09312/) (2017)
- [Sim-to-Real via Sim-to-Sim: Data-efficient Robotic Grasping via Randomized-to-Canonical Adaptation Networks](https://mlsanity.com/paper/1812.07252/) (2018)
- [Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping](https://mlsanity.com/paper/1709.07857/) (2017)
- [Convolutional Networks with Dense Connectivity](https://mlsanity.com/paper/2001.02394/) (2020)
- [Large-scale 6D Object Pose Estimation Dataset for Industrial Bin-Picking](https://mlsanity.com/paper/1912.12125/) (2019)
- [Single Shot 6D Object Pose Estimation](https://mlsanity.com/paper/2004.12729/) (2020)
- [Defining the Pose of any 3D Rigid Object and an Associated Distance](https://mlsanity.com/paper/1612.04631/) (2016)
- [Dex-Net 3.0: Computing Robust Robot Vacuum Suction Grasp Targets in Point Clouds using a New Analytic Model and Deep Learning](https://mlsanity.com/paper/1709.06670/) (2017)
- [Symmetry Aware Evaluation of 3D Object Detection and Pose Estimation in Scenes of Many Parts in Bulk](https://mlsanity.com/paper/1806.08129/) (2018)
- [Real-Time 6D Object Pose Estimation on CPU](https://mlsanity.com/paper/1811.08588/) (2018)
- [Transferring Experience from Simulation to the Real World for Precise Pick-And-Place Tasks in Highly Cluttered Scenes](https://mlsanity.com/paper/2101.04781/) (2021)
- [Investigations on Output Parameterizations of Neural Networks for Single Shot 6D Object Pose Estimation](https://mlsanity.com/paper/2104.07528/) (2021)
- [Automatic Grasp Pose Generation for Parallel Jaw Grippers](https://mlsanity.com/paper/2104.11660/) (2021)

## 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.