AI system enhances household robots’ problem solving skills by up to 80%

It can teach your new household robot how to make coffee in half the time it usually takes.
Loukia Papadopoulos
Representational image of a household robot.jpg
Representational image of a household robot.


MIT researchers have developed PIGINet, a new system that aims to efficiently enhance the problem-solving capabilities of household robots, reducing planning time by 50-80 percent. 

This is according to a press release by the institution published on Friday. 

Under normal conditions, household robots follow predefined recipes for performing tasks, which isn’t always suitable for diverse or changing environments. PIGINet, as described by MIT,  is a neural network that takes in “Plans, Images, Goal, and Initial facts,” then predicts the probability that a task plan can be refined to find feasible motion plans. 

The team evaluated the new system’s ability in helping a robot function in the kitchen. They measured the time taken to solve problems with PIGINet’s assistance against prior approaches. 

They found that PIGINet significantly reduced planning time by 80 percent in simpler scenarios and 20-50 percent in more complex scenarios.

“Systems such as PIGINet, which use the power of data-driven methods to handle familiar cases efficiently, but can still fall back on “first-principles” planning methods to verify learning-based suggestions and solve novel problems, offer the best of both worlds, providing reliable and efficient general-purpose solutions to a wide variety of problems,” said MIT Professor and CSAIL Principal Investigator Leslie Pack Kaelbling.

The researchers also made use of pretrained vision language models and data augmentation tricks to deal with the scarcity of good training data for the household robots.

“Because everyone’s home is different, robots should be adaptable problem-solvers instead of just recipe followers. Our key idea is to let a general-purpose task planner generate candidate task plans and use a deep learning model to select the promising ones. The result is a more efficient, adaptable, and practical household robot, one that can nimbly navigate even complex and dynamic environments. Moreover, the practical applications of PIGINet are not confined to households,” said Zhutian Yang, MIT CSAIL PhD student and lead author on the work. 

“Our 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’s homes.” 

“This paper addresses the fundamental challenge in implementing a general-purpose robot: how to learn from past experience to speed up the decision-making process in unstructured environments filled with a large number of articulated and movable obstacles,” said in the statement Beomjoon Kim PhD ’20, assistant professor in the Graduate School of AI at Korea Advanced Institute of Science and Technology (KAIST).

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