# Task2Morph: Differentiable Task-inspired Framework for Contact-Aware Robot Design

arXiv: https://arxiv.org/abs/2403.19093 · HTML: https://mlsanity.com/paper/2403.19093/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 29 references that are in the map.
- First posted on arXiv 28 Mar 2024.

## Builds on (most cited first, 15 of 15)

- [Prioritized Experience Replay](https://mlsanity.com/paper/1511.05952/) (2015)
- [Embodied Intelligence via Learning and Evolution](https://mlsanity.com/paper/2102.02202/) (2021)
- [MetaMorph: Learning Universal Controllers with Transformers](https://mlsanity.com/paper/2203.11931/) (2022)
- [Jointly Learning to Construct and Control Agents using Deep Reinforcement Learning](https://mlsanity.com/paper/1801.01432/) (2018)
- [An End-to-End Differentiable Framework for Contact-Aware Robot Design](https://mlsanity.com/paper/2107.07501/) (2021)
- [Evolution Gym: A Large-Scale Benchmark for Evolving Soft Robots](https://mlsanity.com/paper/2201.09863/) (2022)
- [Transform2Act: Learning a Transform-and-Control Policy for Efficient Agent Design](https://mlsanity.com/paper/2110.03659/) (2021)
- [Neural Graph Evolution: Towards Efficient Automatic Robot Design](https://mlsanity.com/paper/1906.05370/) (2019)
- [Hardware as Policy: Mechanical and Computational Co-Optimization using Deep Reinforcement Learning](https://mlsanity.com/paper/2008.04460/) (2020)
- [Contrasting Exploration in Parameter and Action Space: A Zeroth-Order Optimization Perspective](https://mlsanity.com/paper/1901.11503/) (2019)
- [Task-Agnostic Morphology Evolution](https://mlsanity.com/paper/2102.13100/) (2021)
- [Fit2Form: 3D Generative Model for Robot Gripper Form Design](https://mlsanity.com/paper/2011.06498/) (2020)
- [Functional Co-Optimization of Articulated Robots](https://mlsanity.com/paper/1707.06617/) (2017)
- [N-LIMB: Neural Limb Optimization for Efficient Morphological Design](https://mlsanity.com/paper/2207.11773/) (2022)
- [C^2:Co-design of Robots via Concurrent Networks Coupling Online and Offline Reinforcement Learning](https://mlsanity.com/paper/2209.06579/) (2022)

## Built on by (most cited first, 4 of 4)

- [MoRE: Unlocking Scalability in Reinforcement Learning for Quadruped Vision-Language-Action Models](https://mlsanity.com/paper/2503.08007/) (2025)
- [HBTP: Heuristic Behavior Tree Planning with Large Language Model Reasoning](https://mlsanity.com/paper/2406.00965/) (2024)
- [Embodied Co-Design for Rapidly Evolving Agents: Taxonomy, Frontiers, and Challenges](https://mlsanity.com/paper/2512.04770/) (2025)
- [CompetEvo: Towards Morphological Evolution from Competition](https://mlsanity.com/paper/2405.18300/) (2024)

## Abstract (arXiv)

Optimizing the morphologies and the controllers that adapt to various tasks is a critical issue in the field of robot design, aka. embodied intelligence. Previous works typically model it as a joint optimization problem and use search-based methods to find the optimal solution in the morphology space. However, they ignore the implicit knowledge of task-to-morphology mapping which can directly inspire robot design. For example, flipping heavier boxes tends to require more muscular robot arms. This paper proposes a novel and general differentiable task-inspired framework for contact-aware robot design called Task2Morph. We abstract task features highly related to task performance and use them to build a task-to-morphology mapping. Further, we embed the mapping into a differentiable robot design process, where the gradient information is leveraged for both the mapping learning and the whole optimization. The experiments are conducted on three scenarios, and the results validate that Task2Morph outperforms DiffHand, which lacks a task-inspired morphology module, in terms of efficiency and effectiveness.