# Designing Tools with Control Confidence

arXiv: https://arxiv.org/abs/2510.12630 · HTML: https://mlsanity.com/paper/2510.12630/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 31 references that are in the map.
- First posted on arXiv 14 Oct 2025.

## Builds on (most cited first, 18 of 18)

- [DeepCAD: A Deep Generative Network for Computer-Aided Design Models](https://mlsanity.com/paper/2105.09492/) (2021)
- [ToolFlowNet: Robotic Manipulation with Tools via Predicting Tool Flow from Point Clouds](https://mlsanity.com/paper/2211.09006/) (2022)
- [Active Inference in Robotics and Artificial Agents: Survey and Challenges](https://mlsanity.com/paper/2112.01871/) (2021)
- [Transform2Act: Learning a Transform-and-Control Policy for Efficient Agent Design](https://mlsanity.com/paper/2110.03659/) (2021)
- [Generating Physically Stable and Buildable Brick Structures from Text](https://mlsanity.com/paper/2505.05469/) (2025)
- [VLMgineer: Vision Language Models as Robotic Toolsmiths](https://mlsanity.com/paper/2507.12644/) (2025)
- [Learning Tool Morphology for Contact-Rich Manipulation Tasks with Differentiable Simulation](https://mlsanity.com/paper/2211.02201/) (2022)
- [Dynamics-Guided Diffusion Model for Sensor-less Robot Manipulator Design](https://mlsanity.com/paper/2402.15038/) (2024)
- [Generating Freeform Endoskeletal Robots](https://mlsanity.com/paper/2412.01036/) (2024)
- [Tool Macgyvering: Tool Construction Using Geometric Reasoning](https://mlsanity.com/paper/1902.03666/) (2019)
- [Learning to Design and Use Tools for Robotic Manipulation](https://mlsanity.com/paper/2311.00754/) (2023)
- [Learning Generalizable Tool-use Skills through Trajectory Generation](https://mlsanity.com/paper/2310.00156/) (2023)
- [Feature Guided Search for Creative Problem Solving Through Tool Construction](https://mlsanity.com/paper/2008.10685/) (2020)
- [RobotSmith: Generative Robotic Tool Design for Acquisition of Complex Manipulation Skills](https://mlsanity.com/paper/2506.14763/) (2025)
- [Computational Design of Passive Grippers](https://mlsanity.com/paper/2306.03174/) (2023)
- [Awareness in robotics: An early perspective from the viewpoint of the EIC Pathfinder Challenge "Awareness Inside''](https://mlsanity.com/paper/2402.09030/) (2024)
- [Confidence-Aware Decision-Making and Control for Tool Selection](https://mlsanity.com/paper/2403.03808/) (2024)
- [Roman: Making Everyday Objects Robotically Manipulable with 3D-Printable Add-on Mechanisms](https://mlsanity.com/paper/2205.07437/) (2022)

## Built on by (most cited first, 1 of 1)

- [Robot Metacognition: Decision Making with Confidence for Tool Invention](https://mlsanity.com/paper/2511.16390/) (2025)

## Abstract (arXiv)

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.