France

Machine Learning and Optimization techniques are now widely used in many scientific disciplines. Using Machine Learning and Optimization in Robotics is a very challenging task as robots may be expensive and fragile, and the time and effort to collect data is, in general, high. Considering these premises, we have developed several techniques that through the use of simulators, and adapted learning/optimization algorithms, that use the data very efficiently, make the use of Learning and Optimization in Robotics an effective option for hand-coded approaches. These techniques have been applied to solve perception, reasoning and behavior level and multi-agent robotic tasks. This talk will present some of these techniques, in different contexts, namely those related to ML/optimization based development of robotic skills, data preparation, Q-Batch update rule, multi-agent learning, adapted interfaces and mixed deep learning/heuristic classification.

ICINCO 2021

Learning and Optimization: Robotic Use Cases

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible. The experience for on-site participants would closely approximate a normal pre-COVID *ACL conference, with 5-6 thematically organized parallel sessions and live Q/A and interactive discussion immediately after the talks. Presentations by virtual participants will be equitably interleaved with those of on-site participants, projected on the auditorium screens as if on-site, and also followed immediately by live Q/A and interactive discussion at a time during reasonable waking hours for the virtual presenter. For all participants, on-site and virtual, who are unable to attend a session due to either time-zone issues or because they are participating in another session live, talk recordings and slides will be available online at a minimum after the live presentation (and in many cases before as well), and questions may be submitted in advance on session-specific discussion boards and answered live in session with the usual visual aids if desired.

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Please Note: The EMNLP registration system is not currently connected to the underline site as we are still in the process of building out EMNLP 2021. You will receive access instructions from underline the week of November 1st. 

Access is given only to EMNLP upon registration, if you have not registered please do so [here](https://2021.emnlp.org/registration).

Registered attendees will receive access the week of November 1st.

EMNLP 2021

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible.

keynote

The purpose of the International Conference on Informatics in Control, Automation and Robotics (ICINCO) is to bring together researchers, engineers and practitioners interested in the application of informatics to Control, Automation and Robotics. Four tracks cover Intelligent Control Systems, Optimization, Robotics, Automation, Signal Processing, Sensors, Systems Modelling and Control, and Industrial Informatics. Informatics applications are pervasive in many areas of Control, Automation and Robotics; This conference intends to emphasize this connection.<br><br>
CONFERENCE AREAS<br>
1 . Intelligent Control Systems and Optimization<br>
2 . Robotics and Automation<br>
3 . Signal Processing, Sensors, Systems Modelling and Control<br>
4 . Industrial Informatics


The purpose of the International Conference on Informatics in Control, Automation and Robotics (ICINCO) is to bring together researchers, engineers and practitioners interested in the application of informatics to Control, Automation and Robotics.

In the past 10 years or so, machine learning has made great progress, in terms of both theories and applications. Among various machine learning methods, deep learning is one of the most widely used methods. Significant advance has been made in speech recognition, object recognition and detection as well as in many other applications using deep learning. In this talk, we present a learning algorithm that is mathematically equivalent to the well-known back-propagation learning algorithm in neural networks but have the following advantages over the back-propagation algorithm. (1) It does not require a feedback network to back-propagate the errors. This makes its implementations, especially implementations on silicon, much simpler. (2) It is biologically plausible as all information needed for synapses to adapt is available in a biological neuron. Hence, artificial neural networks indeed mimic biological neural networks. (3) It can be applied to general dynamic systems, that is, many dynamic systems can adapt in a way similar to neural networks. This property allows us to introduce “intelligence” into a large class of engineering systems. In particular, we consider adaptation in control systems using the proposed learning algorithm, including adaptive state feedback, adaptive PID controller, and model reference adaptive control.

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