# A Design Co-Pilot for Task-Tailored Manipulators

arXiv: https://arxiv.org/abs/2509.13077 · HTML: https://mlsanity.com/paper/2509.13077/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 74 references that are in the map.
- First posted on arXiv 16 Sep 2025.

## Builds on (most cited first, 30 of 33)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation](https://mlsanity.com/paper/1308.3432/) (2013)
- [On the Continuity of Rotation Representations in Neural Networks](https://mlsanity.com/paper/1812.07035/) (2018)
- [Analyzing Inverse Problems with Invertible Neural Networks](https://mlsanity.com/paper/1808.04730/) (2018)
- [Embodied Intelligence via Learning and Evolution](https://mlsanity.com/paper/2102.02202/) (2021)
- [SkexGen: Autoregressive Generation of CAD Construction Sequences with Disentangled Codebooks](https://mlsanity.com/paper/2207.04632/) (2022)
- [Accelerated Policy Learning with Parallel Differentiable Simulation](https://mlsanity.com/paper/2204.07137/) (2022)
- [Reinforcement Learning for Improving Agent Design](https://mlsanity.com/paper/1810.03779/) (2018)
- [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)
- [Physical Design using Differentiable Learned Simulators](https://mlsanity.com/paper/2202.00728/) (2022)
- [DiffuseBot: Breeding Soft Robots With Physics-Augmented Generative Diffusion Models](https://mlsanity.com/paper/2311.17053/) (2023)
- [Transformers are Graph Neural Networks](https://mlsanity.com/paper/2506.22084/) (2025)
- [nvblox: GPU-Accelerated Incremental Signed Distance Field Mapping](https://mlsanity.com/paper/2311.00626/) (2023)
- [Fit2Form: 3D Generative Model for Robot Gripper Form Design](https://mlsanity.com/paper/2011.06498/) (2020)
- [Provably Safe Deep Reinforcement Learning for Robotic Manipulation in Human Environments](https://mlsanity.com/paper/2205.06311/) (2022)
- [GLSO: Grammar-guided Latent Space Optimization for Sample-efficient Robot Design Automation](https://mlsanity.com/paper/2209.11748/) (2022)
- [IKFlow: Generating Diverse Inverse Kinematics Solutions](https://mlsanity.com/paper/2111.08933/) (2021)
- [Efficient automatic design of robots](https://mlsanity.com/paper/2306.03263/) (2023)
- [Graph Neural Networks for Motion Planning](https://mlsanity.com/paper/2006.06248/) (2020)
- [EvoTorch: Scalable Evolutionary Computation in Python](https://mlsanity.com/paper/2302.12600/) (2023)
- [Generative Graphical Inverse Kinematics](https://mlsanity.com/paper/2209.08812/) (2022)
- [RoboBallet: Planning for Multi-Robot Reaching with Graph Neural Networks and Reinforcement Learning](https://mlsanity.com/paper/2509.05397/) (2025)
- [Neural Inverse Kinematics](https://mlsanity.com/paper/2205.10837/) (2022)
- [Comparison of Distal Teacher Learning with Numerical and Analytical Methods to Solve Inverse Kinematics for Rigid-Body Mechanisms](https://mlsanity.com/paper/2003.00225/) (2020)
- [Optimizing Modular Robot Composition: A Lexicographic Genetic Algorithm Approach](https://mlsanity.com/paper/2309.08399/) (2023)
- [Exploring the Potentials and Challenges of Deep Generative Models in Product Design Conception](https://mlsanity.com/paper/2407.11104/) (2024)
- [IK-Geo: Unified Robot Inverse Kinematics Using Subproblem Decomposition](https://mlsanity.com/paper/2211.05737/) (2022)
- [Decoding Modular Reconfigurable Robots: A Survey on Mechanisms and Design](https://mlsanity.com/paper/2310.09743/) (2023)

## Built on by (most cited first, 2 of 2)

- [Embodied Co-Design for Rapidly Evolving Agents: Taxonomy, Frontiers, and Challenges](https://mlsanity.com/paper/2512.04770/) (2025)
- [RAM: Reachability Across Morphologies](https://mlsanity.com/paper/2606.09108/) (2026)

## Abstract (arXiv)

Although robotic manipulators are used in an ever-growing range of applications, robot manufacturers typically follow a ``one-fits-all'' philosophy, employing identical manipulators in various settings. This often leads to suboptimal performance, as general-purpose designs fail to exploit particularities of tasks. The development of custom, task-tailored robots is hindered by long, cost-intensive development cycles and the high cost of customized hardware. Recently, various computational design methods have been devised to overcome the bottleneck of human engineering. In addition, a surge of modular robots allows quick and economical adaptation to changing industrial settings. This work proposes an approach to automatically designing and optimizing robot morphologies tailored to a specific environment. To this end, we learn the inverse kinematics for a wide range of different manipulators. A fully differentiable framework realizes gradient-based fine-tuning of designed robots and inverse kinematics solutions. Our generative approach accelerates the generation of specialized designs from hours with optimization-based methods to seconds, serving as a design co-pilot that enables instant adaptation and effective human-AI collaboration. Numerical experiments show that our approach finds robots that can navigate cluttered environments, manipulators that perform well across a specified workspace, and can be adapted to different hardware constraints. Finally, we demonstrate the real-world applicability of our method by setting up a modular robot designed in simulation that successfully moves through an obstacle course.