Simultaneous estimation of contact position and tool shape with high-dimensional parameters using force measurements and particle filtering

2025-09-23

AI Summary

This work addresses the challenge of jointly estimating tool contact location and high-dimensional (>1000D) discretized (mesh-based) tool geometry during physical contact tasks—a problem where existing approaches either assume known tool geometry, incur prohibitive computational cost, or restrict estimation to low-dimensional parametric models. We propose a unified estimation framework integrating force/torque sensing with particle filtering: each particle encodes a full mesh representation of the tool, and parallel inference is driven solely by observation likelihoods, thereby avoiding direct optimization in the high-dimensional parameter space. To our knowledge, this is the first method enabling real-time, joint estimation of both contact location and explicit >1000D mesh geometry. Extensive simulation and physical experiments demonstrate substantial improvements in contact localization accuracy and successful reconstruction of curved tool surfaces, validating robustness and practicality under occlusion and unstructured environments.

Technology Category

• Intelligent Robots: State Estimation
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Computer Vision: Low Level & Physics-based Vision

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Estimating the contact state between a grasped tool and the environment is essential for performing contact tasks such as assembly and object manipulation. Force signals are valuable for estimating the contact state, as they can be utilized even when the contact location is obscured by the tool. Previous studies proposed methods for estimating contact positions using force/torque signals; however, most methods require the geometry of the tool surface to be known. Although several studies have proposed methods that do not require the tool shape, these methods require considerable time for estimation or are limited to tools with low-dimensional shape parameters. Here, we propose a method for simultaneously estimating the contact position and tool shape, where the tool shape is represented by a grid, which is high-dimensional (more than 1000 dimensional). The proposed method uses a particle filter in which each particle has individual tool shape parameters, thereby to avoid directly handling a high-dimensional parameter space. The proposed method is evaluated through simulations and experiments using tools with curved shapes on a plane. Consequently, the proposed method can estimate the shape of the tool simultaneously with the contact positions, making the contact-position estimation more accurate.

Problem

• Estimating contact positions between tools and environments when tool geometry is unknown
• Simultaneously determining tool shape using high-dimensional parameters from force measurements
• Overcoming limitations of previous methods requiring known tool geometry or low-dimensional shapes

Innovation

• Simultaneously estimates contact position and tool shape
• Uses particle filtering to handle high-dimensional parameters
• Represents tool shape with grid over 1000 dimensions