For a successful deployment of physical Human-Robot Cooperation (pHRC), humans need to be able to teach robots new motor skills quickly. Probabilistic movement primitives (ProMPs) are a promising method to encode a robot’s motor skills learned from human demonstrations in pHRC settings. However, most algorithms to learn ProMPs from human demonstrations operate in batch mode, which is not ideal in pHRC when we want humans and robots to work together from even the first demonstration. In this paper, we propose a new learning algorithm to learn ProMPs incrementally and continuously in pHRC settings. Our algorithm incorporates new demonstrations sequentially as they arrive, allowing humans to observe the robot’s learning progress and incrementally shape the robot’s motor skill. A built-in forgetting factor allows for corrective demonstrations resulting from the human’s learning curve or changes in task constraints. We compare the performance of our algorithm to existing batch ProMP algorithms on reference data generated from a pick-and-place task at our lab. Furthermore, we demonstrate how the forgetting factor allows us to adapt to changes in the task. The incremental learning algorithm presented in this paper has the potential to lead to a more intuitive learning progress and to establish a successful cooperation between human and robot faster than training in batch mode.

Salient Keypoints for Interactive Meta-Learning (SKIML)

This presentation is an overview of the recently developed SpikePropamine meta-learning framework submitted for publication in the Frontiers In Neurorobotics journal. Material from the original paper as well as more recent developments will be presented. 

Our goal in developing this framework is to leverage synaptic plasticity in Spiking Neural Networks (SNNs), the learning mechanism found in biological neural networks. Today, most engineered neural network only use fixed-weights despite the superior learning capabilities found in biological plastic neural networks. Therefore, SpikePropamine is a framework that harnesses the power of offline learning of fixed weights along with the online continual learning through synaptic plasticity. Specifically, along with the fixed weights, SpikePropamine additionally learns the rules that govern the online plastic learning and neuromodulation in SNNs through offline gradient descent. The capabilities of the framework are demonstrated through a series of challenging benchmarks. The parameters for variants of BCM, Oja’s and other plasticity and neuromodulated rules are learned. Results show that the SpikePropamine based networks are able to solve temporal learning tasks that fixed-weight SNNs fail to solve. Additional results highlight the robustness of SpikePropamine based SNNs to noise and novel conditions not seen during training. This is seen through minimal degradation of performance in a high-dimensional legged locomotion task governed by a SpikePropamine based SNN.


SpikePropamine: Meta-Learning Synaptic Plasticity for Spiking Neural Networks

**Session Chairs: Wallace Lawson, Fumihide Tanaka**

Long Session III: Non-verbal Cues and Expressiveness

technical paper

**31st IEEE International Conference on Robot & Human Interactive Communication**
 This conference is a leading forum where state-of-the-art innovative results, the latest developments as well as future perspectives relating to robot and human interactive communication are presented and discussed.
 
 The conference covers a wide range of topics related to Robot and Human Interactive Communication, involving theories, methodologies, technologies, empirical and experimental studies. Papers related to the study of robotic technology, psychology, cognitive science, artificial intelligence, human factors, ethics and policies, interaction-based robot design and other topics related to human-robot interaction are welcome.
 
 **CONFERENCE THEME:**
 Social, asocial, and antisocial robots
 
 Robots are starting to populate our lives: at work, in our homes, hotels, shops. However, not everybody is comfortable with robots finding a place in society. For example, people may be afraid that robots will replace humans in their jobs. This belief is exacerbated by the media that, on one side, raise expectations of robots’ capabilities, while amplifying fears and worries on the other side.
 
 In the next years, due to the increasing request for interaction with people in social environments, academic research and market analyses predict that robots will become more and more “social”. But, at the same time, robots may incur the risk of being perceived as “asocial” as their pervasiveness in everyday environments may become an obstacle to social interaction between humans. Or they may even be rejected as “antisocial” if researchers do not take care to develop robots that behave according to the customs and habits of the communities where they are expected to operate.
 
 To prevent this from happening, robotic researchers can no longer take the luxury of being mere technicians. They must be capable of addressing all the challenges raised by future robot-populated communities in a multidisciplinary way. They need to share their knowledge with the public to make people aware of what robots really are, what expectations we can have from them, the risks we may incur, and the possible mitigation plans.
 
 While being open to any kind of contribution related to Human-Robot Interactive Communication, the RO-MAN 2022 conference will emphasize these aspects of robotic research. How can we create robots capable of being welcomed as our assistants and companions in society while, at the same time, not being perceived as “asocial” or “antisocial?” How can we create robots perceived as facilitators of the natural development of human beings and communities, rather than obstacles?
 
 Contributions from different disciplines, not exclusively related to technological development, will be welcomed as crucial to pave the way to global acceptance of future robots with an informed attitude.
 
 [![](https://assets.underline.io/uploads/markdown_image/1/image/af259abe105b3791c498ae1552d75710.png)](https://drive.google.com/file/d/18w73BZPB_JVOgcrcdZDqYpEupwVt4CnU/view?usp=sharing)

A registration is required to access this event page. If you have not already done so, please use the below link to register for the event. 

[Register Here](http://www.smile.unina.it/ro-man2022/welcome-to-ieee-ro-man-2022/)

Registration Is Required

IEEE RO-MAN 2022

31st IEEE International Conference on Robot & Human Interactive Communication
This conference is a leading forum where state-of-the-art innovative results, the latest developments as well as future perspectives relating to robot and human interactive communication are presented and discussed.

**Moderator:** Riley Zeller-Townson

**Speakers:**

[Vendant Karia](https://www.underline.io/speakers/77582-vedant-karia)

[Joe Hays](https://www.underline.io/speakers/77740-joe-hays)

Angel Yanguas-Gil

[Siddharth Joshi](https://www.underline.io/speakers/61064-siddharth-joshi)

[Justin Mauger](https://www.underline.io/speakers/80736-justin-mauger)

Neuromorphic Computing

Naval Information Warfare Center Pacific is partnering with AFCEA San Diego chapter to host the workshop.

The NAML workshop is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team.

The goals of NAML are:

* To spread awareness of current machine learning research relevant to Naval applications

* To connect machine learning researchers with experts in military requirements

* To build and strengthen collaborations within the defense research community

**Watch this video to hear about the AFCEA purpose.**

<iframe width=1280 height=706 frameborder="0" scrolling="no" src="https://screencast-o-matic.com/player/creleVVcvRh?v=6&ff=1&title=0&controls=1" allowfullscreen="true"></iframe>

Ed Lawson

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Presentations

Salient Keypoints for Interactive Meta-Learning (SKIML)

SpikePropamine: Meta-Learning Synaptic Plasticity for Spiking Neural Networks

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