LLMs for Robotic Object Disambiguation
This work addresses object disambiguation challenges in robotics, offering a method to enhance decision-making, though it is incremental as it builds on existing LLM capabilities with prompt engineering improvements.
The study tackled the problem of using large language models (LLMs) for robotic object disambiguation in tabletop environments, resulting in a few-shot prompt engineering system that improved the LLM's ability to generate disambiguating queries and navigate decision trees to correctly identify objects, even with identical options.
The advantages of pre-trained large language models (LLMs) are apparent in a variety of language processing tasks. But can a language model's knowledge be further harnessed to effectively disambiguate objects and navigate decision-making challenges within the realm of robotics? Our study reveals the LLM's aptitude for solving complex decision making challenges that are often previously modeled by Partially Observable Markov Decision Processes (POMDPs). A pivotal focus of our research is the object disambiguation capability of LLMs. We detail the integration of an LLM into a tabletop environment disambiguation task, a decision making problem where the robot's task is to discern and retrieve a user's desired object from an arbitrarily large and complex cluster of objects. Despite multiple query attempts with zero-shot prompt engineering (details can be found in the Appendix), the LLM struggled to inquire about features not explicitly provided in the scene description. In response, we have developed a few-shot prompt engineering system to improve the LLM's ability to pose disambiguating queries. The result is a model capable of both using given features when they are available and inferring new relevant features when necessary, to successfully generate and navigate down a precise decision tree to the correct object--even when faced with identical options.