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The Coordination Language Facility: coordination of distributed objects
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| Jean-Marc Andreoli, Stephen Freeman, Remo Pareschi |
| Journal of Theory and Practice of Object Systems (TAPOS), 2(2), pp 77-94, 1996 |
| home.pdf.gz |
| home.ps.gz |
The development of communication infrastructures and the rapid growth of networking facilities in information technologies increase information mobility and the decentralisation of work processes in industry and services. This evolution leads to increasing demands on the coordination of information systems. However, information technologies available today are capable of supporting only interoperability of information systems from the point of view of communication infrastructures. This makes possible an easy exchange of information but provides no support for coordination. To fill this gap, we propose the Coordination Language Facility (CLF) as a coordination layer on top of distributed systems infrastructures such as CORBA-compliant Object Request Brokers. The CLF provides support for the coordination of heterogeneous, possibly distributed, active objects within larger units implementing (work) processes. On one hand, coordinator objects are declaratively implemented as rules. On the other hand, the objects participating in a coordination (participants) must instantiate a minimal interface which specifies the negotiation dialogue invoked, at run-time, by coordinators. The coordination activity is split between the implementation of the interface on the participantsside and the execution of the rules on the coordinators side, thus offering a clear separation of concerns between local and global activities. The interface is specified using the CORBA standard for distributed objects, removing issues of heterogeneity and allowing each component to be implemented in the most appropriate language and environment.
To make robots autonomous in real-world everyday spaces, they should be able to learn from their interactions within these spaces, how to best execute tasks specified by non-expert users in a safe and reliable way. To do so requires sequential decision-making skills that combine machine learning, adaptive planning and control in uncertain environments as well as solving hard combinatorial optimisation problems. Our research combines expertise in reinforcement learning, computer vision, robotic control, sim2real transfer, large multimodal foundation models and neural combinatorial optimisation to build AI-based architectures and algorithms to improve robot autonomy and robustness when completing everyday complex tasks in constantly changing environments.
VISION
The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
Our work covers the spectrum from unsupervised to supervised approaches, and from very deep architectures to very compact ones. We’re excited about the promise of big data to bring big performance gains to our algorithms but also passionate about the challenge of working in data-scarce and low-power scenarios.
Furthermore, we believe that a modern computer vision system needs to be able to continuously adapt itself to its environment and to improve itself via lifelong learning. Our driving goal is to use our research to deliver embodied intelligence to our users in robotics, autonomous driving, via phone cameras and any other visual means to reach people wherever they may be.
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