# Prolonging Tool Life: Learning Skillful Use of General-purpose Tools through Lifespan-guided Reinforcement Learning

arXiv: https://arxiv.org/abs/2507.17275 · HTML: https://mlsanity.com/paper/2507.17275/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods.
- Builds on 11 of its 42 references that are in the map.
- First posted on arXiv 23 Jul 2025.

## Placed in

- Deep reinforcement learning methods / [RL for robot locomotion and manipulation](https://mlsanity.com/deeprl/rl-for-robot-locomotion-and-manipulation/)

## Builds on (most cited first, 11 of 11)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Policy Distillation](https://mlsanity.com/paper/1511.06295/) (2015)
- [KETO: Learning Keypoint Representations for Tool Manipulation](https://mlsanity.com/paper/1910.11977/) (2019)
- [Creative Robot Tool Use with Large Language Models](https://mlsanity.com/paper/2310.13065/) (2023)
- [Leveraging Language for Accelerated Learning of Tool Manipulation](https://mlsanity.com/paper/2206.13074/) (2022)
- [GIFT: Generalizable Interaction-aware Functional Tool Affordances without Labels](https://mlsanity.com/paper/2106.14973/) (2021)
- [How to select and use tools? : Active Perception of Target Objects Using Multimodal Deep Learning](https://mlsanity.com/paper/2106.02445/) (2021)
- [DefGraspSim: Physics-based simulation of grasp outcomes for 3D deformable objects](https://mlsanity.com/paper/2203.11274/) (2022)
- [Cyclic Policy Distillation: Sample-Efficient Sim-to-Real Reinforcement Learning with Domain Randomization](https://mlsanity.com/paper/2207.14561/) (2022)
- [Understanding Physical Effects for Effective Tool-use](https://mlsanity.com/paper/2206.14998/) (2022)
- [A Reinforcement Learning Approach to Health Aware Control Strategy](https://mlsanity.com/paper/2010.09269/) (2020)

## Abstract (arXiv)

In inaccessible environments with uncertain task demands, robots often rely on general-purpose tools that lack predefined usage strategies. These tools are not tailored for particular operations, making their longevity highly sensitive to how they are used. This creates a fundamental challenge: how can a robot learn a tool-use policy that both completes the task and prolongs the tool's lifespan? In this work, we address this challenge by introducing a reinforcement learning (RL) framework that incorporates tool lifespan as a factor during policy optimization. Our framework leverages Finite Element Analysis (FEA) and Miner's Rule to estimate Remaining Useful Life (RUL) based on accumulated stress, and integrates the RUL into the RL reward to guide policy learning toward lifespan-guided behavior. To handle the fact that RUL can only be estimated after task execution, we introduce an Adaptive Reward Normalization (ARN) mechanism that dynamically adjusts reward scaling based on estimated RULs, ensuring stable learning signals. We validate our method across simulated and real-world tool use tasks, including Object-Moving and Door-Opening with multiple general-purpose tools. The learned policies consistently prolong tool lifespan (up to 8.01x in simulation) and transfer effectively to real-world settings, demonstrating the practical value of learning lifespan-guided tool use strategies.