Web 3D viewers are typically based on hierarchical scene graphs. In applications where users are allowed to make significant changes to the arrangement of objects, thereby changing the scene graph, those parts of the graph which model the independent objects may need to be restructured in order to represent the transient relationships between the objects.We present the Virtue3D System version 2.0, an application for viewing and manipulating 3D scenes. The Virtue3D Player API and the associated VRL scene representation depend on scene hierarchies for defining models, but, unlike a typical scene graph, they rely on a mechanism of an "Assembly" and "Parts" at the level of the entire scene. This supports dynamic "attachments" between objects that more closely simulate realworld interaction and allow useful modeling and manipulation functions. The Virtue3D System is also designed to provide an efficient interface for applying collision constraints and other types of motion constraints. This is supported by defining "collision groups" and constraints at the level of the Parts in the Assembly.
It would be useful for machines to use computers as humans do so that they can aid us in everyday tasks. This is a setting in which there is also the potential to leverage large-scale expert demonstrations and human judgements of interactive behaviour, which are two ingredients that have driven much recent success in AI. Here we investigate the setting of computer control using keyboard and mouse, with goals specified via natural language. Instead of focusing on hand-designed curricula and specialized action spaces, we focus on developing a scalable method centered on reinforcement learning combined with behavioural priors informed by actual human-computer interactions. We achieve state-of-the-art and human-level mean performance across all tasks within the MiniWob++ benchmark, a challenging suite of computer control problems, and find strong evidence of cross-task transfer. These results demonstrate the usefulness of a unified human-agent interface when training machines to use computers. Altogether our results suggest a formula for achieving competency beyond MiniWob++ and towards controlling computers, in general, as a human would.
Creating agents that can interact naturally with humans is a common goal in artificial intelligence (AI) research. However, evaluating these interactions is challenging: collecting online human-agent interactions is slow and expensive, yet faster proxy metrics often do not correlate well with interactive evaluation. In this paper, we assess the merits of these existing evaluation metrics and present a novel approach to evaluation called the Standardised Test Suite (STS). The STS uses behavioural scenarios mined from real human interaction data. Agents see replayed scenario context, receive an instruction, and are then given control to complete the interaction offline. These agent continuations are recorded and sent to human annotators to mark as success or failure, and agents are ranked according to the proportion of continuations in which they succeed. The resulting STS is fast, controlled, interpretable, and representative of naturalistic interactions. Altogether, the STS consolidates much of what is desirable across many of our standard evaluation metrics, allowing us to accelerate research progress towards producing agents that can interact naturally with humans. https://youtu.be/YR1TngGORGQ
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