Goto

Collaborating Authors

 situational


CARMA: Context-Aware Situational Grounding of Human-Robot Group Interactions by Combining Vision-Language Models with Object and Action Recognition

Deigmoeller, Joerg, Hasler, Stephan, Agarwal, Nakul, Tanneberg, Daniel, Belardinelli, Anna, Ghoddoosian, Reza, Wang, Chao, Ocker, Felix, Zhang, Fan, Dariush, Behzad, Gienger, Michael

arXiv.org Artificial Intelligence

-- We introduce CARMA, a system for situational grounding in human-robot group interactions. Effective collaboration in such group settings requires situational awareness based on a consistent representation of present persons and objects coupled with an episodic abstraction of events regarding actors and manipulated objects. This calls for a clear and consistent assignment of instances, ensuring that robots correctly recognize and track actors, objects, and their interactions over time. T o achieve this, CARMA uniquely identifies physical instances of such entities in the real world and organizes them into grounded triplets of actors, objects, and actions. T o validate our approach, we conducted three experiments, where multiple humans and a robot interact: collaborative pouring, handovers, and sorting. These scenarios allow the assessment of the system's capabilities as to role distinction, multi-actor awareness, and consistent instance identification. Our experiments demonstrate that the system can reliably generate accurate actor-action-object triplets, providing a structured and robust foundation for applications requiring spatiotemporal reasoning and situated decision-making in collaborative settings.


Artificial Intelligence, Machine Learning, and Human Beings

#artificialintelligence

Hmm… so how close can a machine come to "meaning-making?" Had to look into the term. The Meaning Making Model The Meaning Making Model identifies two levels of meaning, global and situational (Park & Folkman, 1997). Global meaning refers to individuals' general orienting systems and view of many situations, while situational meaning refers to meaning regarding a specific instance. Situational meaning comprises initial appraisals of the situation, the revision of global and appraised meanings, and the outcomes of these processes. Components of the Meaning Making Model are illustrated in Figure 1.


#Open #IoT with #Blockchain #AI and #BigData – Paradigm Interactions

#artificialintelligence

There will be many people who will say it does exist and has working technologies, hardware and software. It is an interesting error in thinking to focus on closed system devices/products as to what Ubiquity (IoT3) is. Devices are used to get across the point of various types of connections and networks being accessed. But more importantly in a full implementation of the concept of Ubiquity (often described as the IoT) devices may not even be owned anymore. The ownership of devices ceases to be important if you can own your digital identity, can verify it and establish your own ecosystem of assets in Blockchain.


#Open #IoT with #Blockchain #AI and #BigData – Paradigm Interactions

@machinelearnbot

There will be many people who will say it does exist and has working technologies, hardware and software. It is an interesting error in thinking to focus on closed system devices/products as to what Ubiquity (IoT3) is. Devices are used to get across the point of various types of connections and networks being accessed. But more importantly in a full implementation of the concept of Ubiquity (often described as the IoT) devices may not even be owned anymore. The ownership of devices ceases to be important if you can own your digital identity, can verify it and establish your own ecosystem of assets in Blockchain.


Real or virtual? The two faces of machine learning

#artificialintelligence

There's a lot of sci-fi-level buzz lately about smart machines and software bots that will use big data and the Internet of things to become autonomous actors, such as to schedule your personal tasks, drive your car or a delivery truck, manage your finances, ensure compliance with and adjust your medical activities, build and perhaps even design cars and smartphones, and of course connect you to the products and services that it decides you should use. But there's another path that gets much less attention: the real world. It too uses AI, analytics, big data, and the Internet of things (aka the industrial Internet in this context), though not in the same manner. Whether you're looking to choose a next-frontier career path or simply understand what's going on in technology, it's important to note the differences. A recent conversation with Colin Parris, the chief scientist at manufacturing giant General Electric, crystalized in my mind the different paths that the combination of machine learning, big data, and IoT are on.