Designing Tools with Control Confidence

2025-10-14

AI Summary

Existing autonomous tool design frameworks focus solely on performance optimization, neglecting agents’ confidence in repeated tool usage—leading to insufficient robustness under environmental uncertainty. This paper addresses autonomous robotic hand tool design by introducing, for the first time, a neuro-inspired control confidence mechanism, explicitly incorporated into the optimization objective to jointly balance task accuracy and operational robustness. We propose a neural optimization framework integrating CMA-ES with task-conditioned adaptation, validated in a simulated robotic arm environment. Experiments demonstrate that our method achieves significantly enhanced tool robustness and cross-perturbation reuse reliability with fewer optimization iterations, outperforming mainstream optimization baselines. The core contribution lies in modeling control confidence as an explicit, differentiable design dimension—establishing a novel paradigm for robust tool design in embodied agents.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Solvers and Tools

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Prehistoric humans invented stone tools for specialized tasks by not just maximizing the tool's immediate goal-completion accuracy, but also increasing their confidence in the tool for later use under similar settings. This factor contributed to the increased robustness of the tool, i.e., the least performance deviations under environmental uncertainties. However, the current autonomous tool design frameworks solely rely on performance optimization, without considering the agent's confidence in tool use for repeated use. Here, we take a step towards filling this gap by i) defining an optimization framework for task-conditioned autonomous hand tool design for robots, where ii) we introduce a neuro-inspired control confidence term into the optimization routine that helps the agent to design tools with higher robustness. Through rigorous simulations using a robotic arm, we show that tools designed with control confidence as the objective function are more robust to environmental uncertainties during tool use than a pure accuracy-driven objective. We further show that adding control confidence to the objective function for tool design provides a balance between the robustness and goal accuracy of the designed tools under control perturbations. Finally, we show that our CMAES-based evolutionary optimization strategy for autonomous tool design outperforms other state-of-the-art optimizers by designing the optimal tool within the fewest iterations. Code: https://github.com/ajitham123/Tool_design_control_confidence.

Problem

• Current tool design frameworks lack consideration of agent confidence for repeated use
• Existing autonomous tool design focuses solely on performance optimization without robustness
• Need to balance robustness and accuracy in robotic tool design under uncertainties

Innovation

• Introducing neuro-inspired control confidence into tool design
• Using CMAES-based evolutionary optimization for autonomous tool creation
• Balancing robustness and accuracy with control confidence objective