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Robotics Collaborative Technology Alliance

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Robert Michael S Dean
added 2 research items
The Robotic Collaborative Technology Alliance (RCTA) seeks to provide adaptive robot capabilities which move beyond traditional metric algorithms to include cognitive capabilities. Key to this effort is the Common World Model, which moves beyond the state-of-the-art by representing the world using metric, semantic, and symbolic information. It joins these layers of information to define objects in the world. These objects may be reasoned upon jointly using traditional geometric, symbolic cognitive algorithms and new computational nodes formed by the combination of these disciplines. The Common World Model must understand how these objects relate to each other. Our world model includes the concept of Self-Information about the robot. By encoding current capability, component status, task execution state, and histories we track information which enables the robot to reason and adapt its performance using Meta-Cognition and Machine Learning principles. The world model includes models of how aspects of the environment behave, which enable prediction of future world states. To manage complexity, we adopted a phased implementation approach to the world model. We discuss the design of “Phase 1” of this world model, and interfaces by tracing perception data through the system from the source to the meta-cognitive layers provided by ACT-R and SS-RICS. We close with lessons learned from implementation and how the design relates to Open Architecture.
With recent advances in robotics technologies and autonomous systems, the idea of human-robot teams is gaining ever-increasing attention. In this context, our research focuses on developing an intelligent robot that can autonomously perform non-trivial, but specific tasks conveyed through natural language. Toward this goal, a consortium of researchers develop and integrate various types of intelligence into mobile robot platforms, including cognitive abilities to reason about high-level missions, perception to classify regions and detect relevant objects in an environment, and linguistic abilities to associate instructions with the robot’s world model and to communicate with human teammates in a natural way. This paper describes the resulting system with integrated intelligence and reports on the latest assessment.
Robert Michael S Dean
added 2 research items
The Robotics Collaborative Technology Alliance (RCTA) seeks to provide adaptive robot capabilities which move beyond traditional metric algorithms to include cognitive capabilities. Key to this effort is the Common World Model, which moves beyond the state-of-the-art by representing the world using semantic and symbolic as well as metric information. It joins these layers of information to define objects in the world. These objects may be reasoned upon jointly using traditional geometric, symbolic cognitive algorithms and new computational nodes formed by the combination of these disciplines to address Symbol Grounding and Uncertainty. The Common World Model must understand how these objects relate to each other. It includes the concept of Self-Information about the robot. By encoding current capability, component status, task execution state, and their histories we track information which enables the robot to reason and adapt its performance using Meta-Cognition and Machine Learning principles. The world model also includes models of how entities in the environment behave which enable prediction of future world states. To manage complexity, we have adopted a phased implementation approach. Phase 1, published in these proceedings in 2013 [1], presented the approach for linking metric with symbolic information and interfaces for traditional planners and cognitive reasoning. Here we discuss the design of “Phase 2” of this world model, which extends the Phase 1 design API, data structures, and reviews the use of the Common World Model as part of a semantic navigation use case.
The Robotics Collaborative Technology Alliance (RCTA) seeks to provide adaptive robot capabilities which move beyond traditional metric algorithms to include cognitive capabilities [1]. Research occurs in 5 main Task Areas: Intelligence, Perception, Dexterous Manipulation and Unique Mobility (DMUM), Human Robot Interaction (HRI), and Integrated Research (IR). This last task of Integrated Research is especially critical and challenging. Individual research components can only be fully assessed when integrated onto a robot where they interact with other aspects of the system to create cross-Task capabilities which move beyond the State of the Art. Adding to the complexity, the RCTA is comprised of 12+ independent organizations across the United States. Each has its own constraints due to development environments, ITAR, “lab” vs “real-time” implementations, and legacy software investments from previous and ongoing programs. We have developed three main components to manage the Integration Task. The first is RFrame, a data-centric transport agnostic middleware which unifies the disparate environments, protocols, and data collection mechanisms. Second is the modular Intelligence Architecture built around the Common World Model (CWM). The CWM instantiates a Common Data Model and provides access services. Third is RIVET, an ITAR free Hardware-In-The-Loop simulator based on 3D game technology. RIVET provides each researcher a common test-bed for development prior to integration, and a regression test mechanism. Once components are integrated and verified, they are released back to the consortium to provide the RIVET baseline for further research. This approach allows Integration of new and legacy systems built upon different architectures, by application of Open Architecture principles.