United States

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**RoboCup 2021 will be Worldwide!**

We are excited to announce that RoboCup 2021 is a fully remote event with RoboCup competitions and activities taking place all over the world. Everyone is invited to participate in, or observe, the [competition leagues](https://2021.robocup.org/leagues) and the [symposium](https://2021.robocup.org/symposium)!

**RoboCup Symposium**

The 24th RoboCup International Symposium will be held in conjunction with RoboCup 2021 in a purely online setting. Scientific papers will be presented reporting innovative, original research with relevance to robotics and artificial intelligence. Within the described scope of topics we also encouraged submissions of high-quality overview articles, papers describing real-world research, and papers reporting theoretical results. In addition to the main track with regular research papers, there will be a special track focused on systems, data, and benchmarks featuring papers describing novel, open-source hardware and software systems, as well as datasets and benchmarks relevant to the community. In addition to research paper presentations, the RoboCup Symposium 2021 includes keynote talks by Dieter Fox, Jean-Paul Laumond, and Stefanie Tellex, and several interactive sessions.

**RoboCup Introduction Video**
<iframe width="995" height="516" src="https://www.youtube.com/embed/Cnh43cCsDKU" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>


Registration is required for entrance into RoboCup 2021. <br><br>
**Fees:** 
* Major Faculty or Symposium Faculty - US$50
* Major Student, Junior Mentor, Junior Student, or Symposium Student - US$20 

***Members of qualified teams:** Please contact your league for your registration password and proceed to register with your team. *<br>
***All non-team members:** Please register as "Symposium Only".*

RoboCup 2021

The 24th RoboCup International Symposium will be held in conjunction with RoboCup 2021 in a purely online setting. Scientific papers will be presented reporting innovative, original research with relevance to robotics and artificial intelligence. Within the described scope of topics we also encouraged submissions of high-quality overview articles, papers describing real-world research, and papers reporting theoretical results. In addition to the main track with regular research papers, there will be a special track focused on systems, data, and benchmarks featuring papers describing novel, open-source hardware and software systems, as well as datasets and benchmarks relevant to the community. In addition to research paper presentations, the RoboCup Symposium 2021 includes keynote talks by Dieter Fox, Jean-Paul Laumond, and Stefanie Tellex, and several interactive sessions.

Mobile robots and many edge AI devices have a need to trade off computational power against power consumption, battery size, and time between charges. Consequently, it is common for such devices to have significantly less computational power than the powerful GPU-based systems typically used to train and evaluate deep neural networks. Object detection is a key aspect of visual perception for robots and edge devices but popular object de-tection architectures that run fastest on GPU based systems or that are de-signed to maximize mAP with large input image sizes may not scale well to edge devices. In this work we evaluate the latency and mAP of several model architectures from the YOLO and SSD families on a range of devices repre-sentative of robot and edge device capabilities. We also evaluate the effect of runtime framework and show that some unexpected large differences can be found. Based on our evaluations we propose new variations of the YOLO-LITE architecture which we show can provide increased mAP at reduced latency.

Faster YOLO-LITE: Faster Object Detection on Robot and Edge Devices

poster

Improving Sample Efficiency in Behavior Learning by Using Sub-optimal Planners for Robots

Learning tools for mobile robots localization using visual landmarks and the extended Kalman filter

Software Package to Model, Plan, and Execute Multi-Robot Tasks

Soft Actor Critic Reinforcement Learning for Autonomous Mobile Robot Navigation

Closing the Reality Gap with Unsupervised Sim-to-Real Image Translation

Dataset and Benchmarking of Real-Time Embedded Object Detection for RoboCup SSL

Enabling Modern Application Development with Swift on Nao and Pepper

rSoccer: A Framework for Studying Reinforcement Learning in Small and VerySmall Size Robot Soccer

Optimized Wireless Control and Telemetry Network for Mobile Soccer Robots

A Telemetry-based PI Tunning Strategy for Low-level Control of an Omnidirectional Mobile Robot

https://d3smihljt9218e.cloudfront.net/transcript/14629/subtitle/42f77eef443d9995b92fc60c6ae79274.vtt

English (automatic)

Premium content

Faster YOLO-LITE: Faster Object Detection on Robot and Edge Devices

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Improving Sample Efficiency in Behavior Learning by Using Sub-optimal Planners for Robots

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Closing the Reality Gap with Unsupervised Sim-to-Real Image Translation