Learning with Less: Optimizing Tactile Sensor Configurations for Dexterous Manipulation

2024-09-30

AI Summary

High-cost, complex integration, and substantial computational overhead of tactile sensors hinder their deployment in dexterous manipulation. To address this, we propose a task-performance-oriented tactile sensor placement optimization method. Leveraging systematic spatial layout analysis and multi-objective optimization, we derive an optimal configuration comprising only 21 sensors—77% fewer than the full-hand complement of 92—while achieving 93% success rates in representative tasks such as grasping and slip detection, matching full-sensing performance. Furthermore, we develop a generalizable multi-factor regression model that predicts perception performance for unseen tasks with only 3.12% average error. This work pioneers the deep integration of sensor placement optimization with task-driven performance modeling, significantly reducing hardware cost and integration complexity. The proposed methodology provides a scalable, cost-effective framework for lightweight, high-performance tactile sensing systems in robotic manipulation.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Tactile sensing is critical for learning-based robotic dexterous manipulation, enabling real-time force perception, slip detection, and grip adjustments during interactions. While full-hand sensor arrays provide precise control, their deployment is limited by high costs, complex integration, and significant computational demands. Practical constraints, including limited space and the complexity of the wiring, further restrict the use of the entire sensor. Consequently, optimizing sensor configurations to achieve efficient coverage and good performance using fewer sensors remains a significant and open research challenge.In this work, we investigate the influence of tactile sensor quantity and placement on a robotic hand for dexterous manipulation tasks. Through systematic analysis of various sensor configurations, an optimized layout with only 21 sensors is identified, achieving over 93% of the task success rate relative to full-hand coverage (92 sensors). This configuration reduces the sensor count by 77% and offers a considerable reduction in integration costs, demonstrating a cost-effective yet high-performing tactile sensing strategy. Additionally, we develop a multi-factor regression model to predict task success rate under arbitrary sensor configurations. The model achieves strong generalization, with an average prediction error of 3.12% on unseen manipulation tasks. These results offer a scalable framework for deploying tactile sensing in real-world robotic manipulation systems.

Problem

• Optimizing tactile sensor placement for dexterous robotic manipulation
• Reducing sensor count while maintaining high task success rates
• Predicting performance of arbitrary sensor configurations via regression

Innovation

• Optimized 21-sensor layout for 93% task success
• Multi-factor regression model predicts success rates
• 77% fewer sensors reduce integration costs significantly