ROAIJun 14, 2023

Toward Grounded Commonsense Reasoning

Stanford
arXiv:2306.08651v220 citationsh-index: 66
Originality Incremental advance
AI Analysis

This addresses the challenge of grounding AI commonsense reasoning in physical environments for robotics applications, though it is incremental in combining existing models with active perception.

The paper tackles the problem of enabling robots to perform commonsense reasoning grounded in the real world, such as deciding not to disassemble a Lego car while tidying, by proposing an approach that combines LLMs and VLMs for active perception. The result shows a 12.9% improvement on a new benchmark dataset and a 15% improvement in robot experiments over baselines without active perception.

Consider a robot tasked with tidying a desk with a meticulously constructed Lego sports car. A human may recognize that it is not appropriate to disassemble the sports car and put it away as part of the "tidying." How can a robot reach that conclusion? Although large language models (LLMs) have recently been used to enable commonsense reasoning, grounding this reasoning in the real world has been challenging. To reason in the real world, robots must go beyond passively querying LLMs and actively gather information from the environment that is required to make the right decision. For instance, after detecting that there is an occluded car, the robot may need to actively perceive the car to know whether it is an advanced model car made out of Legos or a toy car built by a toddler. We propose an approach that leverages an LLM and vision language model (VLM) to help a robot actively perceive its environment to perform grounded commonsense reasoning. To evaluate our framework at scale, we release the MessySurfaces dataset which contains images of 70 real-world surfaces that need to be cleaned. We additionally illustrate our approach with a robot on 2 carefully designed surfaces. We find an average 12.9% improvement on the MessySurfaces benchmark and an average 15% improvement on the robot experiments over baselines that do not use active perception. The dataset, code, and videos of our approach can be found at https://minaek.github.io/grounded_commonsense_reasoning.

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