Integrating Human Awareness into Robot Task Planning with Large Language Models

Core Concepts

A novel approach to incorporate human awareness into robot task planning by leveraging Large Language Models (LLMs) and 3D scene graphs.

Abstract

The paper proposes a novel approach to enable human awareness in robot task planning by integrating LLMs and 3D scene graphs. The key contributions are:

Encoding humans and their semantic relationships to other objects in the environment into a hierarchical 3D scene graph representation. Using LLMs, the approach predicts future human activities based on past observations of the scene graph.

Transforming the single-robot task planning problem into a multi-agent task planning problem, where humans are considered as additional planning agents with the predicted activities as their goals. The LLM is used to ground the predicted human activities into formal planning language.

The approach facilitates the integration of human awareness into LLM-driven robot task planning, enabling proactive robot decision-making in dynamic environments with multiple humans.

The architecture consists of the following components:

3D scene graph as the environment representation, with humans modeled as nodes
LLM used to extract domain knowledge and predict future human activities
Automated task planner that generates the final task plan for the robot considering the predicted human activities
The paper outlines the algorithm to transform the single-robot task planning problem into a multi-agent problem by leveraging the LLM predictions. This allows the automated task planner to generate plans that avoid disturbing the humans while the robot completes its tasks.

Stats

The human is likely to open the fridge and fetch something from it.
The fridge contains the items: [cheese, cola, beer...].
Considering past human activities, possible actions are: [(pick cheese), (pick cola), (pick beer)...].
The most probable action is to make spaghetti with noodles, tomato, cheese, oil, pot, etc.
The goal can be formulated with predicate (spaghetti_made).

Quotes

"The recent breakthroughs in the research on Large Language Models (LLMs) have triggered a transformation across several research domains. Notably, the integration of LLMs has greatly enhanced performance in robot Task And Motion Planning (TAMP)."
"Observing such a research gap, in this paper, we propose a novel approach to incorporate human awareness in robot task planning with LLMs."

Key Insights Distilled From

Towards Human Awareness in Robot Task Planning with Large Language Models

by Yuchen Liu,L... at arxiv.org 04-18-2024

https://arxiv.org/pdf/2404.11267.pdf

Deeper Inquiries

How can the proposed approach be extended to handle partial observability of the environment and uncertainty in human behavior prediction

To handle partial observability of the environment and uncertainty in human behavior prediction, the proposed approach can be extended by incorporating probabilistic models and Bayesian reasoning. By integrating techniques such as Partially Observable Markov Decision Processes (POMDPs), the system can account for uncertainty in the environment and human behavior. This involves maintaining a belief state that captures the possible states of the environment and the humans based on observations and predictions. The LLMs can be used to update these beliefs and make decisions that maximize the expected utility considering the uncertainty. Additionally, techniques like Monte Carlo methods can be employed to sample possible future scenarios and make decisions based on these samples, taking into account the uncertainty in human behavior prediction.

What are the potential limitations of using LLMs for human activity prediction, and how can these be addressed

Using LLMs for human activity prediction may have limitations such as biases in the training data, lack of context awareness, and the challenge of capturing complex human behaviors accurately. To address these limitations, it is essential to train the LLMs on diverse and unbiased datasets to reduce biases in predictions. Incorporating contextual information from the environment and past interactions can enhance the accuracy of human behavior predictions. Additionally, ensemble methods can be employed to combine predictions from multiple LLMs to mitigate individual model biases. Furthermore, continual learning techniques can be utilized to adapt the LLMs to changing human behaviors over time, improving prediction accuracy.

How can the integration of human awareness into robot task planning be leveraged to enable more natural and intuitive human-robot collaboration in dynamic environments

The integration of human awareness into robot task planning can enable more natural and intuitive human-robot collaboration in dynamic environments by fostering mutual understanding and cooperation. By considering humans as planning agents with their own goals and constraints, the robot can anticipate human actions and proactively adjust its behavior to facilitate collaboration. This can lead to smoother interactions, reduced conflicts, and improved task efficiency. Moreover, incorporating human awareness can enhance safety in human-robot interactions by enabling the robot to predict and avoid potentially hazardous situations. Overall, leveraging human awareness in robot task planning can create a more harmonious and productive environment for human-robot collaboration.