Learning Human Preferences to Inform Soft Constraints for Robot Planning

Core Concepts

The core message of this work is that human preferences over robot behavior can be effectively learned and represented as soft constraints in a planning framework, enabling adaptable and personalized robot behavior.

Abstract

This work proposes a novel problem formulation for preference learning in human-robot interaction (HRI) where human preferences are encoded as soft planning constraints. The authors explore a data-driven method to enable a robot to infer preferences by querying users, which they instantiate in rearrangement tasks in the Habitat 2.0 simulator. The key highlights and insights are: The authors distinguish between hard constraints (essential for task success) and soft constraints (desired but not required robot behavior) in planning, and focus on learning the soft constraints. The authors represent preferences as a collection of sub-preferences, where each sub-preference corresponds to a specific aspect of the robot's behavior (e.g., order of subtasks, state of receptacles). The authors propose a neural network model to predict the user's preferences given a sequence of queries where the user chooses between potential robot behaviors. The model is trained to predict the probability distribution over the sub-preferences, capturing the uncertainty in the user's choices. The authors evaluate their approach under varied levels of noise in the simulated user choices, and find that models trained on some noise perform better than a perfectly rational baseline, especially when generalizing to different noise levels. The authors also compare models supervised on the ground-truth preferences versus models supervised on the inferred probability distributions, finding that the latter can outperform the former when the training data has high levels of noise. Overall, this work presents a promising approach for learning human preferences as soft constraints in robot planning, paving the way for more adaptable and personalized robot behavior in the future.

Stats

"Preference learning has long been studied in Human-Robot Interaction (HRI) in order to adapt robot behavior to specific user needs and desires." "We focus our work on rearrangement tasks because of their ubiquity across service robotics applications (e.g., robots in the home as in Fig. 1a and 1b, in warehouses, etc.)." "We focus on learning preferences using binary queries in which users indicate their preference among two trajectories of execution of robot behavior because this interaction modality is popular in the HRI literature (e.g., [9], [10], [11], [12])."

Quotes

"We distinguish between such required and desired robot behavior by leveraging a planning framework. Specifically, we propose a novel problem formulation for preference learning in HRI where various types of human preferences are encoded as soft planning constraints." "We show that the proposed approach is promising at inferring three types of preferences even under varying levels of noise in simulated user choices between potential robot behaviors." "Our contributions open up doors to adaptable planning-based robot behavior in the future."

Key Insights Distilled From

Learning Human Preferences Over Robot Behavior as Soft Planning Constraints

by Aust... at arxiv.org 04-01-2024

https://arxiv.org/pdf/2403.19795.pdf

Deeper Inquiries

How could the proposed approach be extended to learn preferences in an online, interactive setting where the robot can actively select the most informative queries to ask the user

To extend the proposed approach to learn preferences in an online, interactive setting where the robot actively selects informative queries, we can implement an active learning strategy. The robot can use uncertainty sampling to decide which queries to ask the user. This involves selecting queries that the model is most uncertain about, thus maximizing the information gained from each interaction. By incorporating active learning, the robot can adapt its query selection strategy based on the current state of knowledge, leading to more efficient preference learning. Additionally, reinforcement learning techniques can be employed to optimize the query selection process, allowing the robot to learn the most relevant preferences quickly.

What are the potential challenges in applying this approach to real-world human-robot interaction scenarios, and how could they be addressed

Applying this approach to real-world human-robot interaction scenarios may pose several challenges. One challenge is the complexity of human preferences, which can be dynamic and context-dependent. Addressing this challenge would require continuous learning and adaptation of the preference model to capture changes in user preferences over time. Another challenge is the potential mismatch between user expectations and the robot's behavior, leading to user dissatisfaction. This could be mitigated by incorporating user feedback mechanisms to refine the preference model and improve the robot's behavior accordingly. Furthermore, ensuring the privacy and security of user data during preference learning is crucial in real-world applications. Implementing robust data protection measures and transparency in data handling can help address these concerns.

How could the representation of preferences as soft constraints in planning be leveraged to enable robots to explain their behavior to users in an interpretable manner

The representation of preferences as soft constraints in planning can be leveraged to enable robots to explain their behavior to users in an interpretable manner. By mapping user preferences to soft constraints in the planning framework, robots can provide explanations for their actions based on the user's preferences. This can be achieved by generating human-readable explanations that link the robot's behavior to the user's stated preferences. Additionally, visual aids such as diagrams or interactive interfaces can be used to illustrate how the robot's actions align with the user's preferences. By providing transparent and interpretable explanations, robots can enhance user trust and acceptance in human-robot interaction scenarios.