Conditionally Combining Robot Skills using Large Language Models (2310.17019v1)

Abstract: This paper combines two contributions. First, we introduce an extension of the Meta-World benchmark, which we call "Language-World," which allows a LLM to operate in a simulated robotic environment using semi-structured natural language queries and scripted skills described using natural language. By using the same set of tasks as Meta-World, Language-World results can be easily compared to Meta-World results, allowing for a point of comparison between recent methods using LLMs and those using Deep Reinforcement Learning. Second, we introduce a method we call Plan Conditioned Behavioral Cloning (PCBC), that allows finetuning the behavior of high-level plans using end-to-end demonstrations. Using Language-World, we show that PCBC is able to achieve strong performance in a variety of few-shot regimes, often achieving task generalization with as little as a single demonstration. We have made Language-World available as open-source software at https://github.com/krzentner/language-world/.

Essay on "Conditionally Combining Robot Skills using LLMs"

The paper "Conditionally Combining Robot Skills using LLMs" presents two significant contributions in the domain of robotics and artificial intelligence. The authors introduce an innovative extension to the Meta-World benchmark, termed "Language-World," enabling the evaluation and integration of LLMs with robotic skills in simulated environments. Additionally, the paper proposes a novel method dubbed Plan Conditioned Behavioral Cloning (PCBC) that leverages high-level plans for efficient few-shot task generalization.

Language-World: Enhancing the Meta-World Benchmark

The Language-World benchmark expands the Meta-World, a well-regarded simulated environment for robotic tasks, by facilitating experiments with LLMs. This extension provides three primary components essential for conducting quantitative comparisons between methodologies that utilize LLMs and those that employ traditional deep reinforcement learning methods:

1. Task Descriptions: Each task in Language-World is coupled with a concise natural-language description, allowing these descriptions to condition multi-task policies or serve as LLM inputs.
2. Query Answering Function (QAF): A robust function capable of evaluating semi-structured textual queries in a Meta-World state. This tool includes evaluating geometric relationships, handling negations, conjunctions, and enabling efficient task evaluation by LLMs that do not integrate comprehensive visual processing.
3. Scripted Skills: A set of 30 scripted skills with natural language descriptions, retrievable for efficiently performing tasks within MT10-language subset of Meta-World.

These components enable researchers to use Language-World to efficiently evaluate and compare robotic strategies employing LLMs against baseline methods.

Plan Conditioned Behavioral Cloning (PCBC)

PCBC offers an efficient mechanism for integrating LLMs into the robotic learning process through the creation and execution of conditional plans. The method involves:

• Plan Generation: Using an LLM to generate a conditional plan consisting of (condition, skill) tuples where conditions are associated linguistic contexts prompting skill activation.
• Action Selection Mechanism: Utilizing a three-step process involving plan generation, query evaluation, and action decoding. This method employs a softmax-based attention mechanism to decode actions, allowing the fine-tuning of plan behavior through demonstrations.

PCBC's architecture stands out by facilitating the separation of skill selection from the execution, providing transparency and human oversight opportunities in robotic autonomous operation.

Experimental Evaluation and Results

The authors conducted extensive evaluations within the Language-World framework, showcasing PCBC's capacity to generalize tasks with minimal demonstrations. Results indicate PCBC's strong few-shot generalization capabilities across tasks, achieving notable performance using just a single demonstration, outperforming traditional descriptor-conditioned models.

The experiments demonstrate that PCBC enables significant data efficiency improvements over standard reinforcement learning approaches, accomplishing effective task execution with a fraction of the data usually required.

Implications and Future Directions

This research offers promising implications for enhancing robotics' adaptability and proficiency through language-conditioned skill integration. The development of Language-World allows researchers to seamlessly introduce natural language as a component in robotic task definition and manipulation, which could refine human-robot interaction interfaces and autonomous decision-making capacities.

Future work might focus on extending PCBC to incorporate reinforcement learning, particularly Offline RL, to explore and validate the approach's adaptability in real-world settings. Additionally, further enhancements in plan quality through advanced prompting techniques and expanding the framework to leverage real-world visual question answering models will likely contribute to realizing the full potential of language-conditioned robotic control systems.

In conclusion, the paper elevates the discourse on LLMs in robotics, offering a data-efficient, transparent, and versatile approach to bridging language with robot skill execution, laying the foundation for more intelligent and adaptive robotic systems.