On the Design of Human-Robot Collaboration Gestures

Effective communication between humans and collaborative robots is essential for seamless Human-Robot Collaboration (HRC). In noisy industrial settings, nonverbal communication, such as gestures, plays a key role in conveying commands and information to robots efficiently. While existing literature has thoroughly examined gesture recognition and robots' responses to these gestures, there is a notable gap in exploring the design of these gestures. The criteria for creating efficient HRC gestures are scattered across numerous studies. This paper surveys the design principles of HRC gestures, as contained in the literature, aiming to consolidate a set of criteria for HRC gesture design. It also examines the methods used for designing and evaluating HRC gestures to highlight research gaps and present directions for future research in this area.

On Self-Supervised Dynamic Incremental Regularised Adaptation

In this paper, we overview a recent method for dynamic domain adaptation named DIRA, which relies on a few samples in addition to a regularisation approach named elastic weight consolidation to achieve state-of-the-art (SOTA) domain adaptation results. DIRA has been previously shown to perform competitively with SOTA unsupervised adaption techniques. However, a limitation of DIRA is that it relies on labels to be provided for the few samples used in adaption. This makes it a supervised technique. In this paper, we discuss a proposed alteration to the DIRA method to make it self-supervised i.e. remove the need for providing labels. Experiments on our proposed alteration will be provided in future work.

Soft Gripping: Specifying for Trustworthiness

Soft robotics is an emerging technology in which engineers create flexible devices for use in a variety of applications. In order to advance the wide adoption of soft robots, ensuring their trustworthiness is essential; if soft robots are not trusted, they will not be used to their full potential. In order to demonstrate trustworthiness, a specification needs to be formulated to define what is trustworthy. However, even for soft robotic grippers, which is one of the most mature areas in soft robotics, the soft robotics community has so far given very little attention to formulating specifications. In this work, we discuss the importance of developing specifications during development of soft robotic systems, and present an extensive example specification for a soft gripper for pick-and-place tasks for grocery items. The proposed specification covers both functional and non-functional requirements, such as reliability, safety, adaptability, predictability, ethics, and regulations. We also highlight the need to promote verifiability as a first-class objective in the design of a soft gripper.

Evaluation Metrics for CNNs Compression

There is a lot of research effort devoted by researcher into developing different techniques for neural networks compression, yet the community seems to lack standardised ways of evaluating and comparing between different compression techniques, which is key to identifying the most suitable compression technique for different applications. In this paper we contribute towards standardisation of neural network compression by providing a review of evaluation metrics. These metrics have been implemented into NetZIP, a standardised neural network compression bench. We showcase some of the metrics reviewed using three case studies focusing on object classification, object detection, and edge devices.

Assessing Trustworthiness of Autonomous Systems

As Autonomous Systems (AS) become more ubiquitous in society, more responsible for our safety and our interaction with them more frequent, it is essential that they are trustworthy. Assessing the trustworthiness of AS is a mandatory challenge for the verification and development community. This will require appropriate standards and suitable metrics that may serve to objectively and comparatively judge trustworthiness of AS across the broad range of current and future applications. The meta-expression `trustworthiness' is examined in the context of AS capturing the relevant qualities that comprise this term in the literature. Recent developments in standards and frameworks that support assurance of autonomous systems are reviewed. A list of key challenges are identified for the community and we present an outline of a process that can be used as a trustworthiness assessment framework for AS.

AERoS: Assurance of Emergent Behaviour in Autonomous Robotic Swarms

The behaviours of a swarm are not explicitly engineered. Instead, they are an emergent consequence of the interactions of individual agents with each other and their environment. This emergent functionality poses a challenge to safety assurance. The main contribution of this paper is a process for the safety assurance of emergent behaviour in autonomous robotic swarms called AERoS, following the guidance on the Assurance of Machine Learning for use in Autonomous Systems (AMLAS). We explore our proposed process using a case study centred on a robot swarm operating a public cloakroom.

Towards a Measure of Trustworthiness to Evaluate CNNs During Operation

Due to black box nature of Convolutional neural networks (CNNs), the continuous validation of CNN classifiers' during operation is infeasible. As a result this makes it difficult for developers or regulators to gain confidence in the deployment of autonomous systems employing CNNs. We introduce the trustworthiness in classification score (TCS), a metric to assist with overcoming this challenge. The metric quantifies the trustworthiness in a prediction by checking for the existence of certain features in the predictions made by the CNN. A case study on persons detection is used to to demonstrate our method and the usage of TCS.

Using Neural Networks for Novelty-based Test Selection to Accelerate Functional Coverage Closure

Machine learning (ML) has been used to accelerate the closure of functional coverage in simulation-based verification. A supervised ML algorithm, as a prevalent option in the previous work, is used to bias the test generation or filter the generated tests. However, for missing coverage events, these algorithms lack the positive examples to learn from in the training phase. Therefore, the tests generated or filtered by the algorithms cannot effectively fill the coverage holes. This is more severe when verifying large-scale design because the coverage space is larger and the functionalities are more complex. This paper presents a configurable framework of test selection based on neural networks (NN), which can achieve a similar coverage gain as random simulation with far less simulation effort under three configurations of the framework. Moreover, the performance of the framework is not limited by the number of coverage events being hit. A commercial signal processing unit is used in the experiment to demonstrate the effectiveness of the framework. Compared to the random simulation, the framework can reduce up to 53.74% of simulation time to reach 99% coverage level.

On Specifying for Trustworthiness

As autonomous systems are becoming part of our daily lives, ensuring their trustworthiness is crucial. There are a number of techniques for demonstrating trustworthiness. Common to all these techniques is the need to articulate specifications. In this paper, we take a broad view of specification, concentrating on top-level requirements including but not limited to functionality, safety, security and other non-functional properties. The main contribution of this article is a set of high-level intellectual challenges for the autonomous systems community related to specifying for trustworthiness. We also describe unique specification challenges concerning a number of application domains for autonomous systems.