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RobotPerf is an open reference benchmarking suite that is used to evaluate robotics computing performance fairly with ROS 2 as its common baseline, so that robotic architects can make informed decisions about the hardware and software components of their robotic systems.

The project's mission is to build open, fair and useful robotics benchmarks that are technology agnostic, vendor-neutral and provide unbiased evaluations of robotics computing performance for hardware, software, and services. As a reference performance benchmarking suite in robotics, RobotPerf can be used to evaluate robotics computing performance across compute substratrated including CPUs, GPUs, FPGAs and other compute accelerators. The benchmarks are designed to be representative of the performance of a robotic system and to be reproducible across different robotic systems. For that, RobotPerf builds on top of ROS 2, the de facto standard for robot application development.

Refer to the Benchmark Specification for more details.

The myriad combinations of robot hardware and robotics software make assessing robotic-system performance challenging, specially in an architecture-neutral, representative, and reproducible manner. RobotPerf addresses this issue delivering a reference performance benchmarking suite that is used to evaluate robotics computing performance across CPU, GPU, FPGA and other compute accelerators.

RobotPerf benchmarks aim to cover the complete robotics pipeline including perception, localization, control, manipulation and navigation. In time, new benchmarks will be added and new categories may appear over time. If you wish to contribute a new benchmark, please read the contributing guidelines.

a Perception	b Localization	c Control	d Navigation	e Manipulation

ID	Graph	Summary	Metric	Hardware	Value	Category	Timestamp	Note	Data Source
a1		Perception computational graph composed by 2 dataflow-connected Components, rectify and resize.	latency (ms)	ROBOTCORE	66.82	edge	14-10-2022		perception/image
a1		Perception computational graph composed by 2 dataflow-connected Components, rectify and resize.	latency (ms)	Kria KR260	66.82	edge	14-10-2022		perception/image
a1		Perception computational graph composed by 2 dataflow-connected Components, rectify and resize.	latency (ms)	Jetson Nano	238.13	edge	14-10-2022		perception/image
a1		Perception computational graph composed by 2 dataflow-connected Components, rectify and resize.	latency (ms)	Jetson AGX Xavier	106.34	edge	14-10-2022		perception/image
a5		Perception resize ROS Component.	latency (ms)	Intel® Core™ i5-8250U CPU @ 1.60GHz × 8	33.68	workstation	08-05-2023		perception/image2
a3		Perception computational graph to compute a disparity map for stereo images.	latency (ms)	12th Gen Intel(R) Core(TM) i9-12900KF	132.12	edge	25-04-2023	Mean: 26.25 ms, RMS: 27.18 ms, Max: 132.12 ms, Min: 8.73 ms over 1124 samples.	perception/image3
a2		Perception rectify ROS Component.	latency (ms)	ROBOTCORE	66.82	edge	14-10-2022		perception/image

Start by reading the benchmark specification. You can contribute to the project both as an individual or as a organization. If you are an individual, feel free to contribute by running the benchmarks and/or by contributing with new benchmarks (see spec). Once ready, submit a pull request and/or raise issues as appropriate. If you are an organization willing to commit resources to the project and contribute to it, please contact here.

For getting involved in the project and/or receiving support to run the RobotPerf benchmarks, contact here.