Much in the vein of ancient times people asking Pythia about their future, I asked ChatGPT about our possibilities in the CORESENSE project to create machines capable of understanding their environment. This is what it said:
Me: Can a machine understand its environment?
ChatGPT: Whether a machine can understand its environment depends on its capabilities and design. Some machines, such as certain types of robots or artificial intelligence systems, are designed to gather and process information about their environment in order to make decisions and take actions. These machines may be able to build a model or representation of their environment and use this information to navigate and interact with it. However, the extent to which these machines can truly “understand” their environment is a complex and contentious topic that is not fully understood. Some argue that machines can only simulate understanding and do not have the same level of conscious awareness and comprehension as humans. Others believe that machines can achieve true understanding through advanced algorithms and learning systems. Ultimately, the ability of a machine to understand its environment depends on its design and capabilities.
ChatGPT is an AI-based chatbot developed by OpenAI to have a dialogue much a la Turing test. The program is a very large English language model that has been fine-tuned with both supervised and reinforcement learning techniques.
We have signed a grant agreement with the EC to coordinate a new research project on intelligent robotics. The CoreSense project will develop a new hybrid cognitive architecture to make robots capable of understanding and being aware of what is going on. The project will start on October 1, 2022 will span four years (2022-2026) and joins six partners across europe in an effort to push forward the limits of robotic cognition.
Cognitive robots are augmenting their autonomy, enabling them to deployments in increasingly open-ended environments. This offers enormous possibilities for improvements in human economy and wellbeing. However, it also poses strong risks that are difficult to assess and control by humans. The trend towards increased autonomy conveys augmented problems concerning reliability, resilience, and trust for autonomous robots in open worlds. The essence of the problem can be traced to robots suffering from a lack of understanding of what is going on and a lack of awareness of their role in these situations. This is a problem that artificial intelligence approaches based on machine learning are not addressing well. Autonomous robots do not fully understand their open environments, their complex missions, their intricate realizations, and the unexpected events that affect their performance. An improvement in the capability to understand of autonomous robots is needed.
The CoreSense project tries to provide a solution to this need in the form of a AI theory of understanding, a theory of robot awareness, some enginering-grade reusable software assets to apply these theories in real robots. The project will build three demonstrations of its capability to augment resilience of drone teams, augment flexibility of manufacturing robots, and augment human alignment of social robots.
In summary, CoreSense will develop a cognitive architecture for autonomous robots based on a formal concept of understanding, supporting value-oriented situation understanding and self-awareness to improve robot flexibility, resilience and explainability.
There are six project partners:
Universidad Politécnica de Madrid – ES – Coordinator Delft University of Technology – NL Fraunhofer IPA – DE Universidad Rey Juan Carlos – ES PAL Robotics – ES Irish Manufacturing Research – IR
Our dissemination team has prepared another movie describing some robotics activities inside the ROBOMINERS Horizon 2020 project. In the movie we see activity at UPM (Spain), TalTech (Estonia), and TAU (Finland) in some of the themes related to ROBOMINERS.
We at UPM are involed in two main activities: the implementation of the RM2 robot miner pratotype and the construction of the high-level autonomy software.
The investigation about what are the best architectures for mind construction has too many intervening threads and interferences. Sometimes heterogeneous people of different domains get together to try to clarify some of the issues concerning mind architecture. In many of these gatherings, people from science and technology try to devise strategies for building computational models or system archures to create artificial minds.
One of these efforts was the EU Funded ICEA Project. The IST 027819 ICEA Integrating Cognition, Emotion and Autonomy was a four-year project, funded by IST Cognitive Systems Unit. The Project was focused on brain-inspired cognitive architectures, robotics and embodied cognition, bringing together cognitive scientists, neuroscientists, psychologists, computational modelers, roboticists and control engineers. The primary aim of the project is to develop a novel cognitive systems architecture integrating cognitive, emotional and bioregulatory (self-maintenance) processes, based on the architecture and physiology of the mammalian brain.
The works but extremely interesting and the teams involved were mostly very active and dedicated to the work. However, the results obtained -basically more elaborated scientific ideas- didn’t have a translation in a concrete architectural implementation of a system that could prove the insights. Different systems wer implemented -real, simulated- but the overall picture was a bit lost.
We need stronger tema integration and specially more convergent objectives to get these types of activities produce the expected and potential results. This is not an easy taks however, as the goal of a cognitive psychologist of an industrial control engineer seem so far a part that convergence sounds more lie a myth than a real, strategical possibility.
All this said, something in fully basic: technology departs from science. Mind engineering must depart from mind since and hence, convergence is necessary. This will only happen if we broaden the tagets and pursue a General Theory of Mind not trapped in the details of rat brains or humanoid robot computer-laden hearths.