United States

A stable and fast walk for humanoid robots is a challenging problem. In particular for multiple robots with different degrees of wear, it is necessary to tune several control parameters, which consumes a lot of time and wears down the hardware even more. In this paper, we present the latest improvements of our current walk, which enable a significant boost in the walking speed and stability, basically independent of the robot&#39;s hardware state. Based on a linear inverted pendulum model and thresholds determined statistically, the feet positions are adapted to follow the center of mass&#39; direction of movement and let the robot walk in the direction of a potential fall if necessary. In addition, we ensure that the movement of the swing foot is parallel to the ground with a closed-loop feedback controller over the measured rotation errors of the support foot, without the direct use of the inertial measurement unit. The approaches presented allow for a up to 40% faster walk on different robots of the same type, without the need for a manual calibration.

RoboCup 2021

Step Adjustment for a Robust Humanoid Walk

robocup standard platform league

step adjustment

humanoid walk

**BQIT is a three day annual workshop** aimed at enabling leading UK and international academics and industrial partners to come together; to explore and discuss future ambitions and challenges in the field of **Quantum Information Technologies.** Since our conception in 2014 we have had 230 speakers and panellists who have presented their work and opinions on a range of topics, from quantum theory to innovation in industry.


### Welcome Message
The BQIT:21 team welcomes you to the eighth Bristol Quantum Information Technologies workshop! 

Last year was the first time in our workshop's history that we had gone fully virtual. The global circumstances that continue to impact us all professionally and personally have forced BQIT once again to be delivered as an all-virtual workshop for 2021. The access this creates to colleagues across the world who would otherwise be unable to join us, as well as being able to make the workshop free for all attendees due to the lower costs involved in delivering an online workshop, are just a couple of the extraordinary benefits of meeting online. This also means that we can welcome three times as many attendees to BQIT than previous years.   

We take this opportunity to thank each of our speakers and poster presenters for contributing their technical content, this year's sponsors, whose support this year enabled BQIT:21 to be free to all attendees, and to all of our attendees for joining us online this year. We recognise the extra energy required to stay connected and motivated during these difficult times, and are hugely encouraged to see so many of you willing to continue to connect with us on an online platform. 

It is our hope that you have a productive and enjoyable time while you engage and interact with BQIT:21, and to everyone that has helped take BQIT:21 online, thank you! 

**The BQIT board and organisation team **


![](https://assets.underline.io/uploads/markdown_image/1/image/a12e0c1ad3410144cb48557690e4ecaf.png)
### This year's workshop agenda is available here: [Virtual BQIT:21](https://drive.google.com/file/d/1QwsSM0ztnxC_lgUKf3TbO3GvnI44AQkY/view?usp=sharing)
For further information, please contact us at: **bqit-admin@bristol.ac.uk**

![](https://assets.underline.io/uploads/markdown_image/1/image/a12e0c1ad3410144cb48557690e4ecaf.png)

Bristol Quantum Information Technologies Workshop

A stable and fast walk for humanoid robots is a challenging problem. In particular for multiple robots with different degrees of wear, it is necessary to tune several control parameters, which consumes a lot of time and wears down the hardware even more. In this paper, we present the latest improvements of our current walk, which enable a significant boost in the walking speed and stability, basically independent of the robot's hardware state. Based on a linear inverted pendulum model and thresholds determined statistically, the feet positions are adapted to follow the center of mass' direction of movement and let the robot walk in the direction of a potential fall if necessary. In addition, we ensure that the movement of the swing foot is parallel to the ground with a closed-loop feedback controller over the measured rotation errors of the support foot, without the direct use of the inertial measurement unit. The approaches presented allow for a up to 40% faster walk on different robots of the same type, without the need for a manual calibration.

poster

[![](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**
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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.

In this paper, we propose a method for predicting the path of the ball on the soccer field for the humanoid robots. A cost-function-based k-nearest neighbor regression method is first proposed to account for the part of the prediction which is based on previously observed data. Next, the autoregression method is utilized in order to carry out the prediction based on the current ball path. Finally, these two methods are combined to form the final prediction model. Moreover, two different schemes are introduced based upon the proposed model: fixed and adaptive schemes. In fixed scheme, the prediction is made once during the
initial steps of the motion and is used throughout the whole ball movement. However, in adaptive scheme, autoregression method coefficients are updated in fixed predened steps during the motion. This is beneficial to robustify the prediction against an externally applied disturbance on the ball path. Our proposed method is tested by simulation and practical implementation and the results demonstrate a high precision rate.

Ball Path Prediction for Humanoid Robots: Combination of k-NN Regression and Autoregression Methods

Soccer Simulation 2D (SS2D) is a simulation of a real soccer game in two dimensions.
In soccer, passing behavior is an essential action for keeping the ball in possession of our team and creating goal opportunities.
Similarly, for SS2D, predicting the passing behaviors of both opponents and our teammates helps manage resources and score more goals.
Therefore, in this research, we have tried to address the modeling of passing behavior of soccer 2D players using Deep Neural Networks (DNN) and Random Forest (RF).
We propose an embedded data extraction module that can record the decision-making of agents in an online format.
Afterward, we apply four data sorting techniques for training data preparation.
After, we evaluate the trained models' performance playing against 6 top teams of RoboCup 2019 that have distinctive playing strategies. Finally, we examine the importance of different feature groups on the prediction of a passing strategy.
All results in each step of this work prove our suggested methodology's effectiveness and improve the performance of the pass prediction in Soccer Simulation 2D games ranging from 5\% (e.g., playing against the same team) to 10\% (e.g., playing against Robocup top teams).

Engineering Features to Improve Pass Prediction in Soccer Simulation 2D Games

Human support robots (mobile robots able to perform useful domestic manipulative tasks) might be better accepted by people if they can communicate in ways they naturally understand: e.g. speech, but also facial expressions, postures, among others. Subtle (unconscious) mirroring of nonverbal cues during conversations promotes rapport building, essential for good communication. We investigate whether, as in human-human communication, the ability of a robot to mirror its user's head movements and facial expressions in real time can improve the user's experience with it. We describe the technical integration of a Toyota Human Support Robot (HSR) with a facially expressive 3D embodied conversational agent (ECA) (named ECA-HSR). The HSR and the ECA are aware of the user's head movements and facial emotions, and can mirror them, in real time. We then discuss a user study we designed in which participants interacted with ECA-HSR in a simple social dialog task with three conditions: mirroring of user's head movements, mirroring of user's facial emotions, and mirroring of both user's head movements and facial emotions. Our results suggest that interacting with an ECA-HSR that mirrors both the user's head movements and the facial expressions is preferred over the other conditions. Among other insights, the study revealed that the accuracy of open source, real-time recognition of facial expressions of emotion needs improvement for the best user's acceptance.

Towards building rapport with a Human Support Robot

Fan input and support is an important component in many individual and team sports, ranging from athletics to basketball. Audience interaction provides a consistent impact on the athletes’ performance. The analysis of the crowd noise can provide a global indication on the ongoing game situation, less conditioned by subjective factors that can influence a single fan. In this work, we exploit the collective intelligence of the audience of a robot soccer match to improve the performance of the robot players. In particular, audio features extracted from the crowd noise are used in a Reinforcement Learning process to possibly modify the game strategy. The effectiveness of the proposed approach is demonstrated by experiments on registered crowd noise samples from several past RoboCup SPL matches.

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Ball Path Prediction for Humanoid Robots: Combination of k-NN Regression and Autoregression Methods

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