PIGINet: Enhancing Robots' Problem-Solving Capabilities

Category Machine Learning

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PIGINet is a neural network from MIT CSAIL which uses machine learning to significantly reduce task planning time for household robots. It takes in information from task plans, images, and initial facts to generate a probability that the selected task plan is feasible, and compared against prior approaches, PIGINet can reduce planning time by 80% in simpler scenarios and up to 50% in more complex scenarios. This allows robots to be more efficient in problem solving in various settings and environments.

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Your brand new household robot is delivered to your house, and you ask it to make you a cup of coffee. Although it knows some basic skills from previous practice in simulated kitchens, there are way too many actions it could possibly take—turning on the faucet, flushing the toilet, emptying out the flour container, and so on. But there's a tiny number of actions that could possibly be useful. How is the robot to figure out what steps are sensible in a new situation?It could use PIGINet, a new system that aims to efficiently enhance the problem-solving capabilities of household robots. Researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) are using machine learning to cut down on the typical iterative process of task planning that considers all possible actions. PIGINet eliminates task plans that can't satisfy collision-free requirements, and reduces planning time by 50%–80% when trained on only 300–500 problems.

PIGINet uses a transformer neural network to quickly and efficiently plan tasks for robots

Typically, robots attempt various task plans and iteratively refine their moves until they find a feasible solution, which can be inefficient and time-consuming, especially when there are movable and articulated obstacles. Maybe after cooking, for example, you want to put all the sauces in the cabinet. That problem might take two to eight steps depending on what the world looks like at that moment. Does the robot need to open multiple cabinet doors, or are there any obstacles inside the cabinet that need to be relocated in order to make space? You don't want your robot to be annoyingly slow—and it will be worse if it burns dinner while it's thinking.

PIGINet works by taking information from task plans, images, and initial facts to generate a probability that the selected task plan is feasible

Household robots are usually thought of as following predefined recipes for performing tasks, which isn't always suitable for diverse or changing environments. So, how does PIGINet avoid those predefined rules? PIGINet 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.

In simple terms, it 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.

The success of PIGINet depends on the quality of the image of the environment which might be hindered by varying lighting or obstructions

Keeping things in the kitchen, the team created hundreds of simulated environments, each with different layouts and specific tasks that require objects to be rearranged among counters, fridges, cabinets, sinks, and cooking pots. By measuring the time taken to solve problems, they compared PIGINet against prior approaches. One correct task plan may include opening the left fridge door, removing a pot lid, moving the cabbage from pot to fridge, moving a potato to the fridge, picking up the bottle from the sink, placing the bottle in the sink, picking up the tomato, or placing the tomato. PIGINet significantly reduced planning time by 80% in simpler scenarios and 20%–50% in more complex scenarios that have multiple, interdependent steps.

The research conducted is intended to expand potential applications of domestic robots beyond entertainment and vacuuming

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