Periodic Reporting for period 1 - CONVINCE (CONtext-aware VerifIable dyNamiC dEliberation)
Berichtszeitraum: 2022-10-01 bis 2023-09-30
There is growing expectation for the deployment of robots that can work for extended periods of time in unstructured environments without human intervention. To achieve this goal, robots must be programmed to perform useful tasks and handle unexpected situations by activating contingency plans to remain operational and avoid catastrophic failures.
The key contribution of CONVINCE is to develop cognitive deliberation capabilities that ensure robot operation over extended periods of time without human intervention. CONVINCE provides tools that allow automatic verification of such capabilities at design and run-time. The project will benefit the software developers of the robotic community by developing an integrated toolchain for designing application-specific deliberation systems. These systems can determine the necessary behaviors of the robot to fulfill a given task, taking into account the context in which the robot operates, and the experience gained during previous executions of the same task. The toolchain also allows for the deployment and configuration of the components that are required to execute these behaviors and automate the analysis of behaviors to ensure that they are safe and secure.
The development of CONVINCE is guided by three real-world scenarios, each posing unique challenges for robotic deliberations, which will be used to validate the results of the project.
• Robot Vacuum Cleaner (UC1): a robot that navigates in diverse home environments and should learn from its experience and avoid getting stuck.
• Assembly Robot (UC2): a robot that performs a complex task of assembling parts based on their shape, and should cope with occlusions, difficult lighting conditions, and missing or restrained parts.
• Robotic Museum Guide (UC3): a humanoid robot that guides visitors inside a museum and should interact with human and handle problematic situations that may arise from unpredictable human behavior.
In CONVINCE, a user-centered design approach is followed, where user needs are assessed to establish a set of high-level requirements. These requirements are then scrutinized concerning legal and ethical regulations, such as GDPR, Ethical AI, and the EU Artificial Intelligence Act, and connected to technical KPIs of Use Cases. Additionally, input from developers of robotic software, who are the end-users of the toolchain, is also taken into account and considered in CONVINCE to increase the success of the project and ensure the outputs meet the needs of the users.
The key contribution of CONVINCE is to develop cognitive deliberation capabilities that ensure robot operation over extended periods of time without human intervention. CONVINCE provides tools that allow automatic verification of such capabilities at design and run-time. The project will benefit the software developers of the robotic community by developing an integrated toolchain for designing application-specific deliberation systems. These systems can determine the necessary behaviors of the robot to fulfill a given task, taking into account the context in which the robot operates, and the experience gained during previous executions of the same task. The toolchain also allows for the deployment and configuration of the components that are required to execute these behaviors and automate the analysis of behaviors to ensure that they are safe and secure.
The development of CONVINCE is guided by three real-world scenarios, each posing unique challenges for robotic deliberations, which will be used to validate the results of the project.
• Robot Vacuum Cleaner (UC1): a robot that navigates in diverse home environments and should learn from its experience and avoid getting stuck.
• Assembly Robot (UC2): a robot that performs a complex task of assembling parts based on their shape, and should cope with occlusions, difficult lighting conditions, and missing or restrained parts.
• Robotic Museum Guide (UC3): a humanoid robot that guides visitors inside a museum and should interact with human and handle problematic situations that may arise from unpredictable human behavior.
In CONVINCE, a user-centered design approach is followed, where user needs are assessed to establish a set of high-level requirements. These requirements are then scrutinized concerning legal and ethical regulations, such as GDPR, Ethical AI, and the EU Artificial Intelligence Act, and connected to technical KPIs of Use Cases. Additionally, input from developers of robotic software, who are the end-users of the toolchain, is also taken into account and considered in CONVINCE to increase the success of the project and ensure the outputs meet the needs of the users.
In the first period of activities, CONVINCE has established the basis for the development of the use cases and the toolchain: this involved user needs analysis, ethical and legal assessment and derivation of technical specifications for use-cases and the toolchain.
The primary development efforts revolve around the design of the toolchain, mainly specifying its components and defining their interactions. CONVINCE has chosen to focus on Behavior Trees (BTs) as the formalism for designing and executing robot behaviors due to their increasing adoption in the community and desirable attributes, such as modularity and readability. BTs are used to model tasks from the point of view of the developers and as the output of automatic mechanisms providing refined or contingency plans. The initial activities have given the foundations for the formal verification of behaviors by establishing the definition of modelling languages for tasks, system components and properties.
The primary development efforts revolve around the design of the toolchain, mainly specifying its components and defining their interactions. CONVINCE has chosen to focus on Behavior Trees (BTs) as the formalism for designing and executing robot behaviors due to their increasing adoption in the community and desirable attributes, such as modularity and readability. BTs are used to model tasks from the point of view of the developers and as the output of automatic mechanisms providing refined or contingency plans. The initial activities have given the foundations for the formal verification of behaviors by establishing the definition of modelling languages for tasks, system components and properties.
CONVINCE will advance the state-of-the-art in three robotic software areas:
1) situation understanding and scene perception,
2) task and motion planning and
3) formal techniques for design-time and run-time verification.
CONVINCE adopts an open science strategy to maximize its impact on the robotic community. This involves making scientific results and datasets available on Zenodo (https://zenodo.org/communities/convince-project) whenever possible. The toolchain supports the ROS middleware to encourage adoption by the ROS community. The main components of the toolchain will be made available under an open-source license on Github (https://github.com/convince-project).
Notably, the project has established a ROS Deliberation Working Group to coordinate the development of deliberation frameworks and libraries, as well as, scripting languages, editors and runtimes, and BT tooling within CONVINCE and across the broader community. The working group is currently exploring ways to standardize the semantics of BT control nodes, which will aid the CONVINCE toolchain in model-check the deliberation layer based on BTs. This is expected to result in the creation of common architectural concepts and framework mechanisms, facilitating the integration of different methods.
1) situation understanding and scene perception,
2) task and motion planning and
3) formal techniques for design-time and run-time verification.
CONVINCE adopts an open science strategy to maximize its impact on the robotic community. This involves making scientific results and datasets available on Zenodo (https://zenodo.org/communities/convince-project) whenever possible. The toolchain supports the ROS middleware to encourage adoption by the ROS community. The main components of the toolchain will be made available under an open-source license on Github (https://github.com/convince-project).
Notably, the project has established a ROS Deliberation Working Group to coordinate the development of deliberation frameworks and libraries, as well as, scripting languages, editors and runtimes, and BT tooling within CONVINCE and across the broader community. The working group is currently exploring ways to standardize the semantics of BT control nodes, which will aid the CONVINCE toolchain in model-check the deliberation layer based on BTs. This is expected to result in the creation of common architectural concepts and framework mechanisms, facilitating the integration of different methods.