[I]t employs a transformer encoder, a versatile and state-of-the-art model designed to operate on data sequences. The input sequence, in this case, is information about which task plan it is considering, images of the environment, and symbolic encodings of the initial state and the desired goal. The encoder combines the task plans, image, and text to generate a prediction regarding the feasibility of the selected task plan.<\/p>\n<\/blockquote>\n

The system is largely focused on kitchen-based activities at present. It draws on simulated home environments to build plans that require interactions with various different elements of the environment, like counters, cabinets, the fridge, sinks, etc. The researchers say that in simpler scenarios, PIGINet was able to reduce planning time by 80%. For more complex situations, that number was generally around 20-50%.<\/p>\n

The team suggests that houses are just the start.<\/p>\n

\u201cThe practical applications of PIGINet are not confined to households,\u201d says PhD student, Zhutian Yang. \u201cOur future aim is to further refine PIGINet to suggest alternate task plans after identifying infeasible actions, which will further speed up the generation of feasible task plans without the need of big datasets for training a general-purpose planner from scratch. We believe that this could revolutionize the way robots are trained during development and then applied to everyone\u2019s homes.\u201d<\/p>\n<\/p><\/div>\n

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