RESEARCH BRIEF
Can AI help design the whole robot—not just its behavior?
Robots and robotic arms designed with AI—from circuit boards and software to control and mechanical structure.
This HARD Lab direction studies the problem as an end-to-end system rather than as a single model demo. Current related work provides useful building blocks and baselines [1] [2] [3] [4] [5]; the lab’s goal is to connect them into measurable, reproducible research artifacts.
AI-assisted circuit, firmware, and mechanical co-design
Control, sensing, and hardware integration
Simulation-to-prototype iteration and physical evaluation
PROPOSED WORKFLOW
Build, measure, iterate.
Co-design
Connect mechanical requirements, circuit design, embedded code, and control objectives in a shared design loop.
Simulate
Evaluate candidate designs and behaviors before building or changing a physical prototype.
Build & test
Integrate the hardware and software, then use physical measurements to guide the next iteration.
STUDENT ENTRY POINTS
Ways to start contributing.
- AI-assisted circuit, firmware, and mechanical co-design
- Control, sensing, and hardware integration
- Simulation-to-prototype iteration and physical evaluation
RELATED WORK / 2026-09-10
Selected papers & citations.
Five primary-source papers selected to frame this direction. Links point to the authors’ arXiv records; these are external works, not HARD Lab publications.
5 selected papers · newest first by initial submission
OmniMech: All-in-one Multimodal Mechanical Benchmark for 3D Reconstruction
Evaluates reconstruction of mechanical designs into executable CAD, including engineering constraints rather than appearance alone.
Why it matters here. A model for checking dimensions and tolerances in generated parts. It is not evidence of a fully autonomous robot-design pipeline.
Full citation & BibTeX
Taiting Lu et al. (2026). OmniMech: All-in-one Multimodal Mechanical Benchmark for 3D Reconstruction. arXiv:2608.05539. https://doi.org/10.48550/arXiv.2608.05539
@misc{hardlab_omnimech,
title = {{OmniMech: All-in-one Multimodal Mechanical Benchmark for 3D Reconstruction}},
author = {Taiting Lu and Runze Liu and Ziwei Dong and Sisong Bei and Jingying Zeng and Mingjia Wang and Zhenghao Li and Kaiyuan Lin and Yi-Shan Wu and Yangshoudu Zheng and Hongxing Pan and Kai Zhang and Guoliang Shi and Ling Ma and Yifan Yang and Jiaying Lu and Qi He and Sung-Liang Chen and Yi-Chao Chen and Yincheng Jin and Mahanth Gowda},
year = {2026},
eprint = {2608.05539},
archivePrefix = {arXiv},
doi = {10.48550/arXiv.2608.05539},
url = {https://arxiv.org/abs/2608.05539},
note = {External related work; metadata checked 2026-09-10}
}OmniLayout: A Schematic-Coupled Multimodal Benchmark for Constraint-Aware Geometric Reasoning in PCB Layout
Examines PCB-layout reasoning in conjunction with schematic connectivity, component placement, and geometric constraints.
Why it matters here. Supports evaluation beyond a plausible board image: connectivity, placement constraints, and routability must also be checked.
Full citation & BibTeX
Taiting Lu et al. (2026). OmniLayout: A Schematic-Coupled Multimodal Benchmark for Constraint-Aware Geometric Reasoning in PCB Layout. arXiv:2607.03261. https://doi.org/10.48550/arXiv.2607.03261
@misc{hardlab_omnilayout,
title = {{OmniLayout: A Schematic-Coupled Multimodal Benchmark for Constraint-Aware Geometric Reasoning in PCB Layout}},
author = {Taiting Lu and Kaiyuan Lin and Mingjia Wang and Haolin Ye and Runze Liu and Yuxin Tian and Vahe Melkonyan and Haoyu Wang and Muchuan Wang and Chufan Hong and Yifan Yang and Sung-Liang Chen and Yi-Chao Chen and Yicheng Jin and Mahanth Gowda},
year = {2026},
eprint = {2607.03261},
archivePrefix = {arXiv},
doi = {10.48550/arXiv.2607.03261},
url = {https://arxiv.org/abs/2607.03261},
note = {External related work; metadata checked 2026-09-10}
}pcbGPT: Automatic PCB Schematic Synthesis from Natural Language Requirements
Uses a Python design representation and tool-assisted validation to synthesize editable KiCad schematics from requirements.
Why it matters here. A direct reference for the electronics branch of AI-native robotics, with generated schematics treated as testable artifacts.
Full citation & BibTeX
Tobias King et al. (2026). pcbGPT: Automatic PCB Schematic Synthesis from Natural Language Requirements. arXiv:2606.01188. https://doi.org/10.48550/arXiv.2606.01188
@misc{hardlab_pcbgpt,
title = {{pcbGPT: Automatic PCB Schematic Synthesis from Natural Language Requirements}},
author = {Tobias King and Steven Kehrberg and Michael Beigl and Tobias Röddiger},
year = {2026},
eprint = {2606.01188},
archivePrefix = {arXiv},
doi = {10.48550/arXiv.2606.01188},
url = {https://arxiv.org/abs/2606.01188},
note = {External related work; metadata checked 2026-09-10}
}Physics-in-the-Loop: A Hybrid Agentic Architecture for Validated CAD Engineering Design
Connects agentic CAD design to engineering knowledge and tool-based physical validation.
Why it matters here. Motivates a generate–simulate–measure loop for mechanical parts, rather than accepting a design based on its visual plausibility.
Full citation & BibTeX
Elias Berger et al. (2026). Physics-in-the-Loop: A Hybrid Agentic Architecture for Validated CAD Engineering Design. arXiv:2605.19717. https://doi.org/10.48550/arXiv.2605.19717
@misc{hardlab_physics_loop,
title = {{Physics-in-the-Loop: A Hybrid Agentic Architecture for Validated CAD Engineering Design}},
author = {Elias Berger and Muhammad Usama and Jan Mehlstäubl and Bernhard Saske and Kristin Paetzold-Byhain},
year = {2026},
eprint = {2605.19717},
archivePrefix = {arXiv},
doi = {10.48550/arXiv.2605.19717},
url = {https://arxiv.org/abs/2605.19717},
note = {External related work; metadata checked 2026-09-10}
}RoboGen: Towards Unleashing Infinite Data for Automated Robot Learning via Generative Simulation
Explores generative simulation to produce environments, tasks, and robot-learning experiences.
Why it matters here. Provides a reference for simulation and skill training, not for PCB fabrication or end-to-end mechanical design.
Full citation & BibTeX
Yufei Wang et al. (2023). RoboGen: Towards Unleashing Infinite Data for Automated Robot Learning via Generative Simulation. arXiv:2311.01455. https://doi.org/10.48550/arXiv.2311.01455
@misc{hardlab_robogen,
title = {{RoboGen: Towards Unleashing Infinite Data for Automated Robot Learning via Generative Simulation}},
author = {Yufei Wang and Zhou Xian and Feng Chen and Tsun-Hsuan Wang and Yian Wang and Katerina Fragkiadaki and Zackory Erickson and David Held and Chuang Gan},
year = {2023},
eprint = {2311.01455},
archivePrefix = {arXiv},
doi = {10.48550/arXiv.2311.01455},
url = {https://arxiv.org/abs/2311.01455},
note = {External related work; metadata checked 2026-09-10}
}No selected papers match this filter.