VisualPredicator: Learning Abstract World Models with Neuro-Symbolic Predicates for Robot Planning (2410.23156v2)

Abstract: Broadly intelligent agents should form task-specific abstractions that selectively expose the essential elements of a task, while abstracting away the complexity of the raw sensorimotor space. In this work, we present Neuro-Symbolic Predicates, a first-order abstraction language that combines the strengths of symbolic and neural knowledge representations. We outline an online algorithm for inventing such predicates and learning abstract world models. We compare our approach to hierarchical reinforcement learning, vision-LLM planning, and symbolic predicate invention approaches, on both in- and out-of-distribution tasks across five simulated robotic domains. Results show that our approach offers better sample complexity, stronger out-of-distribution generalization, and improved interpretability.

• The paper introduces a neuro-symbolic method that dynamically learns abstract world models, significantly enhancing sample efficiency and planning generalization.
• It employs vision-language models to generate predicates that combine perceptual and logical insights, enabling robots to form high-level plans from visual inputs.
• The system uses a closed-loop feedback mechanism to refine its predicates and high-level actions, improving robustness and interpretability in complex environments.

Neuro-Symbolic Abstraction for Robot Planning: Analyzing VisualPredicator

Neuro-symbolic systems are increasingly at the forefront of artificial intelligence research, combining the traditional logic-based approaches with the perceptual abilities facilitated by neural networks. This paper titled "VisualPredicator: Learning Abstract World Models with Neuro-Symbolic Predicates for Robot Planning" introduces a novel framework leveraging this concept for robotic planning tasks. It presents a method for efficiently learning abstract world models using Neuro-Symbolic Predicates (NSPs), which integrate symbolic representations with neural perception models.

Overview

The paper proposes NSPs as a first-order abstraction language, designed to enable robots to form perceptually and logically integrated representations of their environments. Unlike purely symbolic or purely neural approaches, NSPs allow for the synthesis of logical operations with perceptual queries, culminating in a robust structure for executing complex robotic tasks. The work distinguishes itself by integrating vision-LLMs (VLMs) within neuro-symbolic frameworks which improves the sample complexity, as evidenced by fewer required interactions for learning efficient task models.

Methodology

The approach commences by outlining an online algorithm that dynamically invents predicates and constructs high-level actions, facilitating the training of robots in diverse environments. The procedural core is constituted by three primary components:

1. Predicate Learning: The system embarks on proposing predicates grounded both in distinct perceptual changes and robust logical assertions. NSPs are allowed to invoke VLMs to query perceptual properties. This sets it apart by capitalizing on neural approaches to infer visual characteristics such as object positioning and object identity within the perceptual flow.
2. Hierarchical Planning: Planning involves formulating abstract high-level plans consisting of learned HLAs, which, when executed, break down into low-level skills. The symbolic task planner employed utilizes heuristics like A* search, tailored by the learned NSPs.
3. Feedback and Adaptation: Integral to the system is a closed-loop mechanism that intersperses learned predicate validation and refinement based on execution outcomes. Upon detecting planning failures (infeasibility or non-satisficing plans), the system adapts by iterating on predicate proposal and refining the execution model.

Results and Implications

The experimentation spans across five meticulously simulated robotics domains, revealing that the proposed neuro-symbolic framework excels significantly in sample efficiency and task generalization compared to hierarchical reinforcement learning and other neural network planning techniques. In particular, the approach exhibits strong out-of-distribution generalization capabilities, indicative of its potential for real-world applications where environments and tasks are less predictable.

The adoption of NSPs also bolsters interpretability, a core aspect that delineates this framework from purely data-driven learning models. The system emphasizes comprehensible predicate formulations, ultimately aiding in debugging and further refinement.

Future Directions

In reflection, while achieving robustness in simulated domains, real-world implementations might encounter challenges such as sensor noise and partial observability. Future research directions could address integrating more comprehensive perception models to mitigate these challenges, thus closing the sim-to-real gap. Moreover, improvements in the efficiency of the online learning algorithm and extending the framework to precisely handle dynamic and partially observable settings could significantly enhance the practicality of NSPs in broader applications.

In conclusion, "VisualPredicator" decisively advances the integration of neuro-symbolic reasoning within robotics, proposing a practical and flexible adaptation strategy for contemporary robotic planning challenges. This methodology not only sets a precedent for integrating symbolic and neural paradigms but also exhibits a scalable solution for planning in varied and complex environments.