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

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arxiv 2011.02398 v1 pith:Z5XDHQ2B submitted 2020-11-04 cs.RO

classification cs.RO
keywords controlresearchframeworkmodularrobotcommandscontrollersfeedback
verification ladder T0 review T1 audit T2 compute T3 formal
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abstract

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 $1$kHz, 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.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Learning Quantized Continuous Controllers for Integer Hardware

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    QAT-trained MuJoCo policies match FP32 returns with 2–3 bit weights/activations and synthesize to microsecond, microjoule integer inference on a small Artix-7 FPGA.

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    cs.RO 2025-05 conditional novelty 6.0 of 10

    RoboCulture couples a general-purpose robot arm with vision-based pipetting, force-guided tip exchange, and behavior-tree decisions to run a 15-hour yeast culture experiment with automated splitting of saturated wells.

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