# CHSEL: Producing Diverse Plausible Pose Estimates from Contact and Free Space Data

arXiv: https://arxiv.org/abs/2305.08042 · HTML: https://mlsanity.com/paper/2305.08042/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 36 references that are in the map.
- First posted on arXiv 14 May 2023.

## Builds on (most cited first, 16 of 16)

- [On the Continuity of Rotation Representations in Neural Networks](https://mlsanity.com/paper/1812.07035/) (2018)
- [Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm](https://mlsanity.com/paper/1608.04471/) (2016)
- [Illuminating search spaces by mapping elites](https://mlsanity.com/paper/1504.04909/) (2015)
- [Covariance Matrix Adaptation for the Rapid Illumination of Behavior Space](https://mlsanity.com/paper/1912.02400/) (2019)
- [Differentiable Quality Diversity](https://mlsanity.com/paper/2106.03894/) (2021)
- [MidasTouch: Monte-Carlo inference over distributions across sliding touch](https://mlsanity.com/paper/2210.14210/) (2022)
- [SegICP: Integrated Deep Semantic Segmentation and Pose Estimation](https://mlsanity.com/paper/1703.01661/) (2017)
- [Soft-Bubble grippers for robust and perceptive manipulation](https://mlsanity.com/paper/2004.03691/) (2020)
- [A Multi-Hypothesis Approach to Pose Ambiguity in Object-Based SLAM](https://mlsanity.com/paper/2108.01225/) (2021)
- [TANDEM3D: Active Tactile Exploration for 3D Object Recognition](https://mlsanity.com/paper/2209.08772/) (2022)
- [A New Framework for Registration of Semantic Point Clouds from Stereo and RGB-D Cameras](https://mlsanity.com/paper/2012.03683/) (2020)
- [Stein ICP for Uncertainty Estimation in Point Cloud Matching](https://mlsanity.com/paper/2106.03287/) (2021)
- [Soft Tracking Using Contacts for Cluttered Objects to Perform Blind Object Retrieval](https://mlsanity.com/paper/2201.10434/) (2022)
- [Diverse Plausible Shape Completions from Ambiguous Depth Images](https://mlsanity.com/paper/2011.09390/) (2020)
- [Simultaneous Contact Location and Object Pose Estimation Using Proprioception and Tactile Feedback](https://mlsanity.com/paper/2206.01245/) (2022)
- [CLASP: Constrained Latent Shape Projection for Refining Object Shape from Robot Contact](https://mlsanity.com/paper/2110.08719/) (2021)

## Built on by (most cited first, 3 of 3)

- [Soft Quality-Diversity Optimization](https://mlsanity.com/paper/2512.00810/) (2025)
- [RUMI: Rummaging Using Mutual Information](https://mlsanity.com/paper/2408.10450/) (2024)
- [Zero-Shot Peg Insertion: Identifying Mating Holes and Estimating SE(2) Poses with Vision-Language Models](https://mlsanity.com/paper/2503.06026/) (2025)

## Abstract (arXiv)

This paper proposes a novel method for estimating the set of plausible poses of a rigid object from a set of points with volumetric information, such as whether each point is in free space or on the surface of the object. In particular, we study how pose can be estimated from force and tactile data arising from contact. Using data derived from contact is challenging because it is inherently less information-dense than visual data, and thus the pose estimation problem is severely under-constrained when there are few contacts. Rather than attempting to estimate the true pose of the object, which is not tractable without a large number of contacts, we seek to estimate a plausible set of poses which obey the constraints imposed by the sensor data. Existing methods struggle to estimate this set because they are either designed for single pose estimates or require informative priors to be effective. Our approach to this problem, Constrained pose Hypothesis Set Elimination (CHSEL), has three key attributes: 1) It considers volumetric information, which allows us to account for known free space; 2) It uses a novel differentiable volumetric cost function to take advantage of powerful gradient-based optimization tools; and 3) It uses methods from the Quality Diversity (QD) optimization literature to produce a diverse set of high-quality poses. To our knowledge, QD methods have not been used previously for pose registration. We also show how to update our plausible pose estimates online as more data is gathered by the robot. Our experiments suggest that CHSEL shows large performance improvements over several baseline methods for both simulated and real-world data.