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+---
+title: "Video-Enhanced Human Activity Understanding"
+date: 2023-11-03T10:35:35-05:00
+subtitle: ""
+tags: ["Subsystem"]
+dropCap: false
+displayInMenu: false
+displayInList: true
+draft: false
+---
+
+In the field of robotics and AI, teaching robots human like daily activities, especially handling
+and manipulating objects, is challenging due to the variety of tasks and conditions. In this software
+paltform, "Video-Enhanced Human Activity Understanding," harnesses advanced machine learning algorithms
+alongside MetaHuman avatars within Unreal Engine simulations. This combination enables robots to grasp
+and execute tasks by interpreting video-generated activity instructions and subsequently simulating these
+activities in a virtual world governed by physical parameters. As a component of the Physics-enabled Virtual
+Demonstration (PVD) framework, our platform offers a lifelike and effective training environment for robots,
+leveraging physical laws to ensure safer and more productive learning outcomes. This innovative approach significantly
+enhances robotic competence in complex activities, effectively narrowing the gap between theoretical
+learning and practical application.
+
+
+
+
+DeepActionObserver: Refining Instructions for objects Manipulation Actions
+---
+
+Robotic agents are tasked with learning diverse manipulation actions, a challenge compounded
+by the variability in object interactions, tool usage, task contexts, and operational environments.
+Addressing the complexity of determining the appropriate execution of these actions, the DeepActionObserver
+framework empowers robots to interpret text instructions and analyze corresponding video demonstrations
+. This process generates symbolic action descriptions, enriched and clarified by video content, aligning
+closely with advanced cognition-enabled robotic control schemes.
+
+DeepActionObserver synergizes two advanced learning and reasoning paradigms: the Multi-Task
+Network and Markov Logic Networks. The Multi-Task Network utilizes convolutional architectures
+to accurately recognize objects, hand positions, and to predict poses and movements. Meanwhile,
+Markov Logic Networks augment the framework's ability to reason by using joint probabilities to navigate
+and clarify instructional content, thus resolving ambiguities and enriching action descriptions.
+This concise description captures the complementary functionalities of these technologies in enhancing
+the framework's overall performance.
+
+DeepActionObserver
+---
+
+
+
+
+
+Physics-enable Virtual Demonstration
+---
+We've been actively engaged in developing the Physics-enabled Virtual Demonstration (PVD) framework, meticulously crafted to
+enhance robotics manipulation activities. PVD functions as an instructional tool for both robots and virtual humans (MetaHumans),
+utilizing advanced machine learning within a controlled, simulated environment to comprehend essential principles of physics.
+This virtual environment meticulously replicates real-world physics, encompassing crucial aspects such as gravity and object interactions.
+The primary aim of PVD is to assist robots and MetaHumans in adapting and learning through practical exercises within these
+highly realistic simulations, significantly improving their efficiency and safety when confronted with real-world scenarios.
+Furthermore, within the PVD framework, the Human Demonstration element serves as a comprehensive guide, systematically breaking
+down human actions within controlled settings. It simplifies these actions into understandable instructions for robots,
+enhancing their understanding of actions, conditions, movements, and the forces involved. This knowledge equips robots
+to plan their actions more effectively, resulting in improved outcomes and reduced errors across various tasks.
+
+Virtual Demonstrations through Human Manipulation Observation
+---
+
+
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+
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+
+