# A Modular Robotic Arm Control Stack for Research: Franka-Interface and FrankaPy

arXiv: https://arxiv.org/abs/2011.02398 · HTML: https://mlsanity.com/paper/2011.02398/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Nov 2020.

## Built on by (most cited first, 25 of 25)

- [RVT: Robotic View Transformer for 3D Object Manipulation](https://mlsanity.com/paper/2306.14896/) (2023)
- [Learning Visible Connectivity Dynamics for Cloth Smoothing](https://mlsanity.com/paper/2105.10389/) (2021)
- [Seeing Glass: Joint Point Cloud and Depth Completion for Transparent Objects](https://mlsanity.com/paper/2110.00087/) (2021)
- [Learning to Grasp the Ungraspable with Emergent Extrinsic Dexterity](https://mlsanity.com/paper/2211.01500/) (2022)
- [BridgeV2W: Bridging Video Generation Models to Embodied World Models via Embodiment Masks](https://mlsanity.com/paper/2602.03793/) (2026)
- [Hierarchical Neural Dynamic Policies](https://mlsanity.com/paper/2107.05627/) (2021)
- [FlowWAM: Optical Flow as a Unified Action Representation for World Action Models](https://mlsanity.com/paper/2607.13017/) (2026)
- [InteRACT: Transformer Models for Human Intent Prediction Conditioned on Robot Actions](https://mlsanity.com/paper/2311.12943/) (2023)
- [OpenVR: Teleoperation for Manipulation](https://mlsanity.com/paper/2305.09765/) (2023)
- [SpatialVAM:Spatial-Aware Multi-View Video Diffusion as a Data-Efficient Robot Policy](https://mlsanity.com/paper/2604.03181/) (2026)
- [Learning Generalizable Tool-use Skills through Trajectory Generation](https://mlsanity.com/paper/2310.00156/) (2023)
- [MResT: Multi-Resolution Sensing for Real-Time Control with Vision-Language Models](https://mlsanity.com/paper/2401.14502/) (2024)
- [BiAssemble: Learning Collaborative Affordance for Bimanual Geometric Assembly](https://mlsanity.com/paper/2506.06221/) (2025)
- [Open-Source Multimodal Moxin Models with Moxin-VLM and Moxin-VLA](https://mlsanity.com/paper/2512.22208/) (2025)
- [Flow-based Domain Randomization for Learning and Sequencing Robotic Skills](https://mlsanity.com/paper/2502.01800/) (2025)
- [Push Past Green: Learning to Look Behind Plant Foliage by Moving It](https://mlsanity.com/paper/2307.03175/) (2023)
- [A3D: Adaptive Affordance Assembly with Dual-Arm Manipulation](https://mlsanity.com/paper/2601.11076/) (2026)
- [Learning Reactive and Predictive Differentiable Controllers for Switching Linear Dynamical Models](https://mlsanity.com/paper/2103.14256/) (2021)
- [Fluid Viscosity Prediction Leveraging Computer Vision and Robot Interaction](https://mlsanity.com/paper/2308.02715/) (2023)
- [Learning Quantized Continuous Controllers for Integer Hardware](https://mlsanity.com/paper/2511.07046/) (2025)
- [Tac2Real: Reliable and GPU Visuotactile Simulation for Online Reinforcement Learning and Zero-Shot Real-World Deployment](https://mlsanity.com/paper/2603.28475/) (2026)
- [DRAPER: Towards a Robust Robot Deployment and Reliable Evaluation for Quasi-Static Pick-and-Place Cloth-Shaping Neural Controllers](https://mlsanity.com/paper/2409.15159/) (2024)
- [GeRaF: Neural Geometry Reconstruction from Radio Frequency Signals](https://mlsanity.com/paper/2605.29097/) (2026)
- [Asynchronous Cooperative Online Learning for Multi-Robot Control under Computational Delays](https://mlsanity.com/paper/2608.29562/) (2026)
- [Event-triggered Control and Online Learning for Networked Systems under Computational Delays](https://mlsanity.com/paper/2608.29576/) (2026)

## Abstract (arXiv)

We designed a modular robotic control stack that provides a customizable and accessible interface to the Franka Emika Panda Research robot. This framework abstracts high-level robot control commands as skills, which are decomposed into combinations of trajectory generators, feedback controllers, and termination handlers. Low-level control is implemented in C++ and runs at 1kHz, and high-level commands are exposed in Python. In addition, external sensor feedback, like estimated object poses, can be streamed to the low-level controllers in real time. This modular approach allows us to quickly prototype new control methods, which is essential for research applications. We have applied this framework across a variety of real-world robot tasks in more than 5 published research papers. The framework is currently shared internally with other robotics labs at Carnegie Mellon University, and we plan for a public release in the near future.