Robots That Ask For Help: Uncertainty Alignment for Large Language Model Planners (2307.01928v2)

Abstract: LLMs exhibit a wide range of promising capabilities -- from step-by-step planning to commonsense reasoning -- that may provide utility for robots, but remain prone to confidently hallucinated predictions. In this work, we present KnowNo, which is a framework for measuring and aligning the uncertainty of LLM-based planners such that they know when they don't know and ask for help when needed. KnowNo builds on the theory of conformal prediction to provide statistical guarantees on task completion while minimizing human help in complex multi-step planning settings. Experiments across a variety of simulated and real robot setups that involve tasks with different modes of ambiguity (e.g., from spatial to numeric uncertainties, from human preferences to Winograd schemas) show that KnowNo performs favorably over modern baselines (which may involve ensembles or extensive prompt tuning) in terms of improving efficiency and autonomy, while providing formal assurances. KnowNo can be used with LLMs out of the box without model-finetuning, and suggests a promising lightweight approach to modeling uncertainty that can complement and scale with the growing capabilities of foundation models. Website: https://robot-help.github.io

Uncertainty Alignment for LLM-Based Robotic Planning

The paper "Robots That Ask For Help: Uncertainty Alignment for LLM Planners" discusses a method to address the uncertainty in planning tasks for robots using LLMs. The authors introduce KnowNo, a framework leveraging conformal prediction (CP) to provide statistical assurances on task completion while minimizing human assistance.

Robots equipped with LLMs can interpret and execute complex language-based instructions, but the propensity of LLMs to generate confidently incorrect outputs—often referred to as "hallucinations"—poses significant challenges. The paper presents a solution that allows robots to identify and acknowledge situations where they lack sufficient confidence, thereby prompting them to request human intervention. This capability is crucial in preventing erroneous actions, especially in ambiguous or novel environments.

Overview of KnowNo

KnowNo builds upon conformal prediction, a statistical method that provides set-based predictions with coverage guarantees. The framework comprises the following key components:

1. Multiple-Choice Generation: Given a scenario, the LLM generates a set of semantically diverse candidate actions.
2. Prediction Set Generation: Conformal prediction is utilized to determine a subset of these actions, ensuring that a specified level of task success is met.
3. Human Assistance: When the prediction set contains multiple actions, the robot requests human clarification.
4. Task Execution: The robot executes the clarified or confirmed action.

One notable advantage of KnowNo is its capability to work with LLMs without the need for task-specific model fine-tuning, making it a lightweight approach adaptable to the increasing capabilities of foundation models.

Experimental Evaluation

The framework's efficacy is evaluated across multiple scenarios involving both simulated and real robotic setups. These scenarios span different modes of ambiguity, including spatial, numeric, and human preference uncertainties. KnowNo consistently outperforms modern baselines, which include ensemble approaches and prompt-tuning strategies. These experiments validate KnowNo's ability to improve efficiency and autonomy while maintaining formal assurances of task completion. Moreover, the approach demonstrates a reduction in human intervention by up to 24% compared to baseline methods.

Theoretical and Practical Implications

Theoretically, KnowNo contributes significantly to the growing literature on the application of conformal prediction in robotics, particularly in settings with sequential decision-making. The extension of conformal prediction to multistep scenarios—where the robot's actions impact future states—provides a formalized methodology for achieving desired task completion rates in dynamic environments.

Practically, the implementation of KnowNo holds promise for enhancing the reliability of autonomous systems in real-world applications, such as household robots or industrial automation. The ability to dynamically align uncertainty with task demands allows for safer interaction in human-populated or complex environments, where ambiguous or incomplete instructions are commonplace.

Future Directions

The authors outline several future avenues for research, including integrating uncertainty estimates from perception modules and exploring active preference learning to refine human-robot interaction further. Additionally, enhanced human error modeling could be incorporated into the framework to account for potential inaccuracies in human intervention.

By addressing the critical challenge of uncertainty in LLM-based robotic planning, KnowNo represents a substantive advance towards the deployment of autonomous systems in unstructured real-world settings. Its blend of theoretical robustness and practical feasibility marks a significant step forward in the field of language-instructed robotics.