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Tool Shape Optimization through Backpropagation of Neural Network

Kento Kawaharazuka, Toru Ogawa, Cota Nabeshima

TL;DR

The paper tackles the problem of optimizing both tool shape and trajectory for robotic tool-use by learning a state-transition model, Tool-Net, that maps the current task state, a binarized tool image, and a trajectory to a predicted next state. Tool-Net enables backpropagation-based optimization to produce tool shapes and motions that realize a target task state, validated through real-world 2D object manipulation experiments. Key contributions include (1) a binarized-image representation for flexible tool shapes, (2) a data-augmentation-rich training pipeline, (3) a gradient-based optimization framework for jointly refining tool shape and trajectory, and (4) demonstration of single- and multi-task applicability with qualitative and quantitative improvements over baselines. The approach paves the way for practical tool-making in robotics by enabling learning-driven, image-based tooling optimization that operates directly on real hardware, with future directions toward 3D extension and incorporation of physical priors.

Abstract

When executing a certain task, human beings can choose or make an appropriate tool to achieve the task. This research especially addresses the optimization of tool shape for robotic tool-use. We propose a method in which a robot obtains an optimized tool shape, tool trajectory, or both, depending on a given task. The feature of our method is that a transition of the task state when the robot moves a certain tool along a certain trajectory is represented by a deep neural network. We applied this method to object manipulation tasks on a 2D plane, and verified that appropriate tool shapes are generated by using this novel method.

Tool Shape Optimization through Backpropagation of Neural Network

TL;DR

The paper tackles the problem of optimizing both tool shape and trajectory for robotic tool-use by learning a state-transition model, Tool-Net, that maps the current task state, a binarized tool image, and a trajectory to a predicted next state. Tool-Net enables backpropagation-based optimization to produce tool shapes and motions that realize a target task state, validated through real-world 2D object manipulation experiments. Key contributions include (1) a binarized-image representation for flexible tool shapes, (2) a data-augmentation-rich training pipeline, (3) a gradient-based optimization framework for jointly refining tool shape and trajectory, and (4) demonstration of single- and multi-task applicability with qualitative and quantitative improvements over baselines. The approach paves the way for practical tool-making in robotics by enabling learning-driven, image-based tooling optimization that operates directly on real hardware, with future directions toward 3D extension and incorporation of physical priors.

Abstract

When executing a certain task, human beings can choose or make an appropriate tool to achieve the task. This research especially addresses the optimization of tool shape for robotic tool-use. We propose a method in which a robot obtains an optimized tool shape, tool trajectory, or both, depending on a given task. The feature of our method is that a transition of the task state when the robot moves a certain tool along a certain trajectory is represented by a deep neural network. We applied this method to object manipulation tasks on a 2D plane, and verified that appropriate tool shapes are generated by using this novel method.
Paper Structure (18 sections, 5 equations, 11 figures, 2 algorithms)

This paper contains 18 sections, 5 equations, 11 figures, 2 algorithms.

Table of Contents

  1. INTRODUCTION
  2. Tool Shape and Trajectory Optimization Network
  3. Network Structure of Tool-Net
  4. Data Collection for Tool-Net
  5. Training Phase of Tool-Net
  6. Optimization Phase of Tool-Net
  7. Detailed Implementation
  8. Experiments
  9. Experimental Setup
  10. Tool Shape and Trajectory Optimization for One Task
  11. Training Phase
  12. Optimization Phase
  13. Evaluation of Optimization Phase
  14. Tool Shape Optimization for Multiple Tasks
  15. Discussion
  16. ...and 3 more sections

Figures (11)

  • Figure 1: Functional diagram of our method where a tool shape and trajectory are optimized for a given task from a current and target task state
  • Figure 2: Network structure of the proposed Tool-Net and optimization procedure of tool shape and trajectory
  • Figure 3: The settings of the robot, task state, and tool state
  • Figure 4: Experimental setup
  • Figure 5: Results of image processing
  • ...and 6 more figures