# Towards a Modular Bin-picking Framework for Handling Object Pose Uncertainties

arXiv: https://arxiv.org/abs/2607.13698 · HTML: https://mlsanity.com/paper/2607.13698/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 37 references that are in the map.
- First posted on arXiv 15 Jul 2026.

## Builds on (most cited first, 21 of 21)

- [BOP: Benchmark for 6D Object Pose Estimation](https://mlsanity.com/paper/1808.08319/) (2018)
- [BOP Challenge 2020 on 6D Object Localization](https://mlsanity.com/paper/2009.07378/) (2020)
- [BOP Challenge 2022 on Detection, Segmentation and Pose Estimation of Specific Rigid Objects](https://mlsanity.com/paper/2302.13075/) (2023)
- [Implicit-PDF: Non-Parametric Representation of Probability Distributions on the Rotation Manifold](https://mlsanity.com/paper/2106.05965/) (2021)
- [BOP Challenge 2023 on Detection, Segmentation and Pose Estimation of Seen and Unseen Rigid Objects](https://mlsanity.com/paper/2403.09799/) (2024)
- [A Smooth Representation of Belief over SO(3) for Deep Rotation Learning with Uncertainty](https://mlsanity.com/paper/2006.01031/) (2020)
- [Robust, Occlusion-aware Pose Estimation for Objects Grasped by Adaptive Hands](https://mlsanity.com/paper/2003.03518/) (2020)
- [Large-scale 6D Object Pose Estimation Dataset for Industrial Bin-Picking](https://mlsanity.com/paper/1912.12125/) (2019)
- [Confronting Ambiguity in 6D Object Pose Estimation via Score-Based Diffusion on SE(3)](https://mlsanity.com/paper/2305.15873/) (2023)
- [Deep Learning a Grasp Function for Grasping under Gripper Pose Uncertainty](https://mlsanity.com/paper/1608.02239/) (2016)
- [SpyroPose: SE(3) Pyramids for Object Pose Distribution Estimation](https://mlsanity.com/paper/2303.05308/) (2023)
- [Ki-Pode: Keypoint-based Implicit Pose Distribution Estimation of Rigid Objects](https://mlsanity.com/paper/2209.09659/) (2022)
- [KeyMatchNet: Zero-Shot Pose Estimation in 3D Point Clouds by Generalized Keypoint Matching](https://mlsanity.com/paper/2303.16102/) (2023)
- [Robotic Task Success Evaluation Under Multi-modal Non-Parametric Object Pose Uncertainty](https://mlsanity.com/paper/2403.10874/) (2024)
- [SE(3)-PoseFlow: Estimating 6D Pose Distributions for Uncertainty-Aware Robotic Manipulation](https://mlsanity.com/paper/2511.01501/) (2025)
- [Two-Stage Grasping: A New Bin Picking Framework for Small Objects](https://mlsanity.com/paper/2303.02604/) (2023)
- [Off-the-shelf bin picking workcell with visual pose estimation: A case study on the world robot summit 2018 kitting task](https://mlsanity.com/paper/2309.16221/) (2023)
- [Corr2Distrib: Making Ambiguous Correspondences an Ally to Predict Reliable 6D Pose Distributions](https://mlsanity.com/paper/2505.02501/) (2025)
- [Good Grasps Only: A data engine for self-supervised fine-tuning of pose estimation using grasp poses for verification](https://mlsanity.com/paper/2409.11512/) (2024)
- [Towards High Precision: An Adaptive Self-Supervised Learning Framework for Force-Based Verification](https://mlsanity.com/paper/2508.02153/) (2025)
- [Object Pose Distribution Estimation for Determining Revolution and Reflection Uncertainty in Point Clouds](https://mlsanity.com/paper/2512.07211/) (2025)

## Abstract (arXiv)

In recent years, there has been growing interest in robust robotic systems for precise bin-picking applications. To achieve reliable performance, such systems must address errors arising from both the object pose estimation and the grasping process. Although various approaches have been proposed, they typically target specific challenges and do not offer general solutions. In this paper, we present a modular framework that jointly handles both error types. The framework incorporates object pose distribution estimation to account for pose uncertainty, which frequently arises in situations with ambiguous observations where a single correct pose cannot be determined. To further reduce uncertainty, we introduce a second-viewpoint module that computes complementary pose distributions, which are subsequently fused. This fusion decreases overall uncertainty and improves system efficiency. Additionally, two independent modules are included to compensate for grasping errors. The modular design allows the components to be combined for optimal performance or used individually, depending on the physical setup. The proposed method is evaluated in a real-world setup with three different objects, with no errors, and all modules are shown to improve efficiency. These results suggest that incorporating pose distributions with grasping pose errors is a promising direction for developing more flexible and reliable robotic production systems. To the best of our knowledge, this is the first framework that jointly addresses both grasping and object pose uncertainties using interchangeable modules. We believe there is ample opportunity to integrate additional modules, resulting in improved performance and flexibility. The current framework is limited to pose uncertainties in SO(2), but it could be extended to SE(3), enabling additional modules to improve the system.