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Found 18 papers, 3 papers with code
A Multi-Level Corroborative Approach for Verification and Validation of Autonomous Robotic Swarms
We explore our approach through a case study focused on a swarm of robots operating within a public cloakroom.
Detecting Stimuli with Novel Temporal Patterns to Accelerate Functional Coverage Closure
Novel test selectors have demonstrated their effectiveness in accelerating the closure of functional coverage for various industrial digital designs in simulation-based verification.
On Self-Supervised Dynamic Incremental Regularised Adaptation
In this paper, we give an overview of a recently developed 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.
Soft Gripping: Specifying for Trustworthiness
Soft robotics is an emerging technology in which engineers create flexible devices for use in a variety of applications.
Evaluation Metrics for DNNs Compression
There is a lot of ongoing research effort into developing different techniques for neural networks compression.
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.
AERoS: Assurance of Emergent Behaviour in Autonomous Robotic Swarms
The behaviours of a swarm are not explicitly engineered.
A Trustworthiness Score to Evaluate DNN Predictions
Due to the black box nature of deep neural networks (DNN), the continuous validation of DNN during operation is challenging with the absence of a human monitor.
Using Neural Networks for Novelty-based Test Selection to Accelerate Functional Coverage Closure
Novel test selectors used in simulation-based verification have been shown to significantly accelerate coverage closure regardless of the number of coverage holes.
On Specifying for Trustworthiness
As autonomous systems (AS) increasingly become part of our daily lives, ensuring their trustworthiness is crucial.
Hybrid Intelligent Testing in Simulation-Based Verification
Efficient and effective testing for simulation-based hardware verification is challenging.
Supervised Learning for Coverage-Directed Test Selection in Simulation-Based Verification
Constrained random test generation is one of the most widely adopted methods for generating stimuli for simulation-based verification.
DIRA: Dynamic Domain Incremental Regularised Adaptation
We show that DIRA improves on the problem of forgetting and achieves strong gains in performance when retraining using a few samples from the target domain.
D-VAL: An automatic functional equivalence validation tool for planning domain models
This paper introduces an approach to validate the functional equivalence of planning domain models.
Run-Time Power Modelling in Embedded GPUs with Dynamic Voltage and Frequency Scaling
This paper investigates the application of a robust CPU-based power modelling methodology that performs an automatic search of explanatory events derived from performance counters to embedded GPUs.
Other Computer Science
Goal-constrained Planning Domain Model Verification of Safety Properties
The verification of planning domain models is crucial to ensure the safety, integrity and correctness of planning-based automated systems.
Model-based Test Generation for Robotic Software: Automata versus Belief-Desire-Intention Agents
In this paper, we compare using Belief-Desire-Intention (BDI) agents as models for test generation with more conventional automata-based techniques that exploit model checking, in terms of practicality, performance, transferability to different scenarios, and exploration (`coverage'), through two case studies: a cooperative manufacturing task, and a home care scenario.
Towards the Safety of Human-in-the-Loop Robotics: Challenges and Opportunities for Safety Assurance of Robotic Co-Workers
The success of the human-robot co-worker team in a flexible manufacturing environment where robots learn from demonstration heavily relies on the correct and safe operation of the robot.
