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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.

RoboCup 2021

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

[![](https://assets.underline.io/uploads/markdown_image/1/image/21d13e03491d7e9acf6b8c46c7979c25.png)](https://docs.google.com/spreadsheets/d/1SCDDfkQk7es4J6RULKHVx1RzbB0Mutla--Sle5SVfb0/edit?usp=sharing)

**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".*

You need to be registered to explore this event!

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.

poster

The design and implementation of behaviors for robots operating in dynamic and complex environments are becoming mandatory in nowadays applications. Reinforcement learning is consistently showing remarkable results in learning effective action policies and in achieving super-human performance in various tasks -- without exploiting prior knowledge. However, in robotics, the use of purely learning-based techniques is still subject to strong limitations. Foremost, sample efficiency. Such techniques, in fact, are known to require large training datasets, and long training sessions, in order to develop effective action policies. Hence in this paper, to alleviate such constraint, and to allow learning in such robotic scenarios, we introduce SErP (Sample Efficient robot Policies), an iterative algorithm to improve the sample-efficiency of learning algorithms. SErP exploits a sub-optimal planner (here implemented with a monitor-replanning algorithm) to lead the exploration of the learning agent through its initial iterations. Intuitively, SErP exploits the planner as an expert in order to enable focused exploration and to avoid portions of the search space that are not effective to solve the task of the robot. Finally, to confirm our insights and to show the improvements that SErP carries with, we report the results obtained in two different robotic scenarios: (1) a cartpole scenario and (2) a soccer-robots scenario within the RoboCup@Soccer SPL environment.


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

This proposal addresses the creation of educational tools to help
students with the concept of ‘localization’ of a mobile robot using
the extended Kalman filter (EKF) and landmarks in the
environment.

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

Multi-robot coordination is one of the fields where RoboCup has consistently provided conditions that promote state-of-the-art advances. Still, it is common to see many teams achieving autonomy by applying hand-crafted rule-based plans. Formal approaches have produced optimistic results when addressing this issue but currently lack available software tools. In this project, we developed a MATLAB® toolbox that provides, through a user-friendly API, methods to model multi-robot coordination problems as generalized stochastic Petri nets with rewards (GSPNRs). This formalism provides a compact way of capturing: action selection, uncertainty on the duration of action execution, and team goals. This toolbox also implements algorithms that allow extracting optimal policies for the GSPNR model, while reasoning over uncertainty and optimizing team-level objectives. Furthermore, it integrates with ROS middleware enabling the execution of real multi-robot systems. We strongly believe that this could significantly benefit the RoboCup initiative as it facilitates the use of state-of-the-art formal methods that enable the improvement of autonomous behaviors in aspects such as efficiency, robustness, reliability, safety, and predictability.

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

Nowadays, the advancement of the mobile robot is also factored in the development of a world surrounded by technologies. Reinforcement Learning is used to enable the system to operate automatically, thus the robot will learn the next movement based on the interaction between the agent and the environment. The main contribution of this paper is to implement Soft Actor-Critic (SAC) to control mobile robot navigation in a 2D simulation. To evaluate the system developed using a 2D simulation with a trained SAC agent. According to this simulation, the process shows that SAC is successful to navigate the mobile robot from an initial to the target location and also can avoid obstacles in the environment.

Soft Actor Critic Reinforcement Learning for Autonomous Mobile Robot Navigation

Deep learning approaches have become the standard solution to many problems in computer vision and robotics, but obtaining sufficient training data in high enough quality is challenging, as human labor is error prone, time consuming, and expensive. Solutions based on simulation have become more popular in recent years, but the gap between simulation and reality is still a major issue. In this paper, we introduce a novel method for augmenting synthetic image data through unsupervised image-to-image translation by applying the style of real world images to simulated images with open source frameworks. The generated dataset is combined with conventional augmentation methods and is then applied to a neural network model running in real-time on autonomous soccer robots. Our evaluation shows a significant improvement compared to models trained on images generated entirely in simulation.

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

When producing a model to object detection in a specific context, the first obstacle is to have a dataset labeling the desired classes. In RoboCup, some leagues already have more than one dataset to train and evaluate a model. However, in the Small Size League (SSL), there is not such dataset available yet. This paper presents an open-source dataset to be used as a benchmark for real-time object detection in SSL. This work also presented a pipeline to train, deploy, and evaluate Convolutional Neural Networks (CNNs) models in a low-power embedded system. This pipeline was used to evaluate the proposed dataset with state-of-art optimized models. In this dataset, the MobileNet SSD v1 achieves 44.88% AP (68.81% AP50) at 94 Frames Per Second (FPS) while running on an SSL robot.

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

We show the advantages of using Swift as the programming language for behaviour on the Pepper and Nao robots as used with the RoboCup Standard Platform Leage and the RoboCup@Home - Social Standard Platform. We show that Swift is not only incoporating modern features of Object-Oriented programming and functional programming, but is also now a stable systems programming language that enables both high-level development as well as fine hardware control.

Enabling Modern Application Development with Swift on Nao and Pepper

Reinforcement learning is an active research area with a vast number of applications in robotics, and the RoboCup competition is an interesting environment for studying and evaluating reinforcement learning methods. A known difficulty in applying reinforcement learning to robotics is the high number of experience samples required, being the use of simulated environments for training the agents followed by transfer learning to real-world (sim-to-real) a viable path. This article introduces an open-source simulator for the IEEE Very Small Size Soccer and the Small Size League optimized for reinforcement learning experiments. We also propose a framework for creating OpenAI Gym environments with a set of benchmarks tasks for evaluating single-agent and multi-agent robot soccer skills. We then demonstrate the learning capabilities of two state-of-the-art reinforcement learning methods as well as their limitations in certain scenarios introduced in this framework. We believe this will make it easier for more teams to compete in these categories using end-to-end reinforcement learning approaches and further develop this research area.

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

In a diverse set of robotics applications, including RoboCup categories, mobile robots require control commands to interact with surrounding environment correctly. These control commands should come wirelessly to not interfere in robots' movement; also, the communication has a set of requirements, including low latency and consistent delivery. This paper presents a complete communication architecture consisting of computer communication with a base station, which transmits the data to robots and returns robots telemetry to the computer. With the proposed communication, it is possible to send messages in less than 4.5ms for six robots with telemetry enables in all of them.

Optimized Wireless Control and Telemetry Network for Mobile Soccer Robots

Mobile robot control requires precision and accuracy to react to unpredictable situations. However, it is non-trivial to design a low-level controller for each wheel that combines fast response and stability in the presence of disturbances. The Proportional-Integral (PI) controller algorithm is the most used technique for DC motors; however, tuning these controllers can become a time-consuming task depending on the number of robots and tuning methodology. We propose a novel telemetry-based strategy to find suitable PI controller parameters more quickly using accurate motor models of the omnidirectional mobile robots obtained from an on-site data sampling mechanism. We evaluate our approach on an omnidirectional robot designed within the RoboCup Small Size League (SSL) competition rules. The results compare the proposed method with a based on Quantitative Feedback Theory (QFT) approach. Our strategy improved on average the robot's performance by 17.95% when using Integral Absolute Error (IAE) and by 12.75% when using Integral Squared Error (ISE) criteria.

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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

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