# Tool as Embodiment for Recursive Manipulation

arXiv: https://arxiv.org/abs/2112.00359 · HTML: https://mlsanity.com/paper/2112.00359/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2021.

## Built on by (most cited first, 5 of 5)

- [Can Wikipedia Help Offline Reinforcement Learning?](https://mlsanity.com/paper/2201.12122/) (2022)
- [Blocks Assemble! Learning to Assemble with Large-Scale Structured Reinforcement Learning](https://mlsanity.com/paper/2203.13733/) (2022)
- [A System for Morphology-Task Generalization via Unified Representation and Behavior Distillation](https://mlsanity.com/paper/2211.14296/) (2022)
- [VLMgineer: Vision Language Models as Robotic Toolsmiths](https://mlsanity.com/paper/2507.12644/) (2025)
- [Learning to Design and Use Tools for Robotic Manipulation](https://mlsanity.com/paper/2311.00754/) (2023)

## Abstract (arXiv)

Humans and many animals exhibit a robust capability to manipulate diverse objects, often directly with their bodies and sometimes indirectly with tools. Such flexibility is likely enabled by the fundamental consistency in underlying physics of object manipulation such as contacts and force closures. Inspired by viewing tools as extensions of our bodies, we present Tool-As-Embodiment (TAE), a parameterization for tool-based manipulation policies that treat hand-object and tool-object interactions in the same representation space. The result is a single policy that can be applied recursively on robots to use end effectors to manipulate objects, and use objects as tools, i.e. new end-effectors, to manipulate other objects. By sharing experiences across different embodiments for grasping or pushing, our policy exhibits higher performance than if separate policies were trained. Our framework could utilize all experiences from different resolutions of tool-enabled embodiments to a single generic policy for each manipulation skill. Videos at https://sites.google.com/view/recursivemanipulation