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Learning Planning Abstractions from Language (2405.03864v1)

Published 6 May 2024 in cs.RO and cs.AI

Abstract: This paper presents a framework for learning state and action abstractions in sequential decision-making domains. Our framework, planning abstraction from language (PARL), utilizes language-annotated demonstrations to automatically discover a symbolic and abstract action space and induce a latent state abstraction based on it. PARL consists of three stages: 1) recovering object-level and action concepts, 2) learning state abstractions, abstract action feasibility, and transition models, and 3) applying low-level policies for abstract actions. During inference, given the task description, PARL first makes abstract action plans using the latent transition and feasibility functions, then refines the high-level plan using low-level policies. PARL generalizes across scenarios involving novel object instances and environments, unseen concept compositions, and tasks that require longer planning horizons than settings it is trained on.

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Summary

  • The paper introduces the PARL framework, which automatically extracts symbolic actions from language to streamline planning processes.
  • It demonstrates a multi-stage methodology that includes symbol discovery, abstract model training, and plan execution, enhancing planning efficiency.
  • The approach improves adaptability in robotics and simulations by generalizing learned abstractions to novel and complex scenarios.

Exploring Abstractions in AI Planning through Language: A Look at PARL

Introduction

The manipulation of abstraction within AI, specifically for planning and learning, has lingered at the forefront of efficiency enhancements in robotics and related fields. Typically, this involves simplifying complex environments into more manageable entities in both state and action representations. In the context of AI, leveraging these abstractions helps an agent to decode and interact with environments in a computationally frugal way.

However, previous methodologies have often leaned on manually defining these abstracted "symbols", which can be labor-intensive and restrict the flexibility of the system. Recent advancements aimed to evolve this by learning these abstractions directly from data, and notably, through natural language inputs.

This blog post explores an innovative framework termed Planning Abstraction from Language (PARL) detailed in a paper. PARL automates the discovery of abstracted action spaces through language-annotated demonstrations, constructs a latent state abstraction, and hones these abstractions to effectively plan and interact within a given environment.

Breaking Down the PARL Framework

PARL's Core Stages:

  1. Symbol Discovery: PARL begins by analyzing language descriptions associated with demonstrations to extract what we call "action" and "object" concepts. These are essentially the building blocks of tasks that need to be performed.
  2. Abstract Model Training: Once symbols are isolated, the next stage is about establishing relationships — learning how actions transition between states in this simplified symbolic "world", gauging the feasibility of particular actions within certain states, and translating these abstract actions into actual controllable actions in the environment (like robotic movement).
  3. Plan Execution: With models in place, PARL can now propose sequences of abstract actions based on real-time observations, predict their outcomes, and calibrate the actions to fulfill given tasks, described in natural language.

Through these stages, PARL promotes a nuanced understanding and interaction with varied environments based purely on symbolic representations and abstracted instructions.

Practical Applications and Implications

The capabilities of PARL extend into areas where robust planning is essential:

  • Robotics: Especially in scenarios where discrete tasks need defining and executing in dynamic environments with precision, like in household robotics or manufacturing lines.
  • Gaming and Simulations: Where characters or agents need to navigate through complex set-ups or storylines by understanding and following abstracted commands.

Practically, what makes PARL especially influential is its ability to generalize this understanding to new, unseen scenarios — say, novel objects or unexpected states not covered in its training data. This aspect becomes critical where variability is frequent or unpredictable.

Theoretical Contributions and Future Prospects

The underlying power of PARL lies in its capability to automate the extraction of high-level abstractions from descriptive language, a feature that both eases the training process and enhances the adaptability of the system across varying tasks and environments. It stands out by enabling a form of "planning by abstraction", supporting faster and more flexible decision-making processes.

As future directions, enhancements could revolve around improving the initialization and segmentation of actions within its input—possibly leveraging unsupervised learning to further free up dependencies on curated data inputs. Moreover, integrating more advanced pre-trained models for object recognition can broaden its applicability to more diverse scenarios, promoting even broader generalization capabilities.

In conclusion, PARL represents a significant step toward embodying language understanding in practical planning and decision-making tasks within artificial intelligence. Its ability to break down and utilize language-based instructions not only streamlines the planning process but also stands as a fertile ground for future explorations into autonomous agent training and operations.

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