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Problem Solving Using a Multi-Strategy Approach

Robot Solving a Lockbox
Lupe

Motivation

Solving diverse tasks in different environments is something humans can do with ease. The same cannot be said about robots. The range of problems that a single robot can solve is usually very narrow. One widely accepted explanation for human superiority in problem-solving is the multi-strategy framework. It is assumed that the mind has a repertoire of different strategies that it uses in solving cognitive and behavioral tasks. The selection of the appropriate strategy depends on the perceived task environmental cues. Although this theory is well studied, the mechanisms underlying the selection of the right strategy from the toolbox are still not well understood. The strategy selection framework as a rational meta-reasoning, introduced by F. Lieder & T. L. Griffith, 2017, proposes a model of how strategy selection can be realized. It assumes that the strategy selection from the toolbox is based on a subjective assessment of the strategy's accuracy and cost, which are in a trade-off against each other: This means that by knowing the accuracy and cost of each strategy, an optimal choice can be made. This method has been analyzed and approved for multiple simple tasks such as gambling. Whether this theory would apply to more natural tasks like escaping an escape room or opening a lockbox is still unknown.

Description of Work

In this work, we want to validate this theory for more realistic tasks like lockboxes and evaluate how robots can benefit from it. Your tasks will include:

  1. Designing some lockboxes with different levels of difficulty.
  2. Creating 3D models of these lockboxes that can be simulated in (MuJoCo or PyBullet).
  3. Implementing a policy that selects from a predefined set of strategies according to the strategy selection framework.
  4. Benchmarking the performance of the created policy on the different lockboxes.

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