Online Neuro-Symbolic Predicate Invention for High-Level Planning
learning abstractions for planning;neuro-symbolic ai;concept learning
Yes
No ethics review needed.
1. How would your method scale to real-world environments with more complex visual scenes and possible noise? Have you considered the potential limitation of current VLMs in these settings, and what could be the effect on NSPs' performance?

2. Considering the fact that VLMs are not infallible, how sensitive is your approach to inaccuracies or biases in the VLM outputs? Do you observe cases where VLM errors lead to incorrect predicate invention or planning failures, and how could these be mitigated?

3. Your approach assumes a given fixed set of low-level motor skills. How much can that assumption be relaxed, and could the approach be extended towards learning or adapting these skills jointly with the high-level predicates?
1. Novel Approach: This paper proposes a novel approach to effectively integrate symbolic reasoning with neural perceptual representations using NSPs to address the challenge of forming task-specific abstractions in robotics.

2. Online Predicate Invention: The proposed method uses online invention of predicates from environment interactions that does not need demonstrations. This is a valuable property in real-world applications, since collecting demonstrations could be expensive or even infeasible.

3. Integration of VLMs: VLMs are leveraged to enable the agent to handle rich representations of concepts, both visually and logically, grounding predicates in the perceptual data, thus enabling more flexible and stronger abstractions.

4. Strong Empirical Results: Their results show strong empirical results, outperforming other baselines over various domains in terms of sample efficiency and generalization to unseen tasks, and planning efficiency.

5. Interpretability: The model uses NSPs, providing interpretable outputs. This helps in understanding or debugging the behavior of the agent. This is critical in safety-critical applications.
This paper introduces Neuro-Symbolic Predicates (NSPs), a first-order abstraction language that combines symbolic and neural knowledge representations for high-level planning in robotics. The authors propose an online algorithm for inventing such predicates and learning abstract world models from interactions with the environment, without relying on demonstrations. The method leverages Vision-Language Models (VLMs) to ground predicates in perceptual data and integrates them into a planning framework that interleaves predicate proposal, validation, and goal-driven exploration. The approach is evaluated across five simulated robotic domains, comparing it with hierarchical reinforcement learning, VLM planning, and symbolic predicate invention methods. The results demonstrate that the proposed method offers better sample efficiency, stronger out-of-distribution generalization, and improved interpretability.
1. **Extension to Stochastic Environments**: How would you extend D-TSN to handle stochastic decision-making problems? What modifications to the current framework would be necessary to accommodate stochastic transitions and rewards?

2. **Computational Efficiency and Scalability**: Can you provide more details on the computational requirements of D-TSN compared to the baselines? How does the method scale with respect to the size of the action space and the depth of the search tree?

3. **Real-World Applications**: Have you considered applying D-TSN to real-world tasks or environments with higher-dimensional observation spaces, such as language or vision? What challenges do you anticipate in such settings, and how might they be addressed?

4. **Comparison with Additional Methods**: Could you include comparisons with other state-of-the-art methods in model-based reinforcement learning or planning? This would help position your work within the broader context and demonstrate its relative advantages.

5. **Impact of Individual Components**: While some ablation studies are provided, could you further analyze the impact of individual components, such as the value function or world model, on the overall performance? Understanding their contributions could provide deeper insights into the method's effectiveness.
Yes
No ethics review needed.
See Weakness
This paper has sufficient quality and soundness, and I'm satisfied with its approach and evaluations.
- The paper is well-structured and clearly-presented. It has good coverage of literature.
- Learning symbolic predicates is foundamental to high-level planning. The paper proposes a flexible approach to learn neural-symbolic predicates grounded on image observations and natural language.
- The proposed method is comprehensively evaluated via comparison with baselines and ablation studies.
This paper presents a new method to learn grounded symbolic predicates and operators from online interaction data for high-level planning. The core of this method is to generate predicates represented in python functions by prompting an VLM. Such predicate functions can invoke VLMs to process image observations and incorporate logical structure to build predicate hierarchies (i.e., derived predicates). Then the most useful predicates are selected and operators are learned following variants of previous works [1, 2]. In experiments, the method is evaluated on 5 domains with multiple baseline methods including Hierarchical RL and VLM planning, showingcase that the method can recover symbolic planning domains that allows effective planning.

[1] Silver, Tom, et al. "Predicate invention for bilevel planning." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 37. No. 10. 2023.

[2] Chitnis, Rohan, et al. "Learning neuro-symbolic relational transition models for bilevel planning. In 2022 IEEE." RSJ International Conference on Intelligent Robots and Systems (IROS).
- I would suppose the VLM-based predicate functions can get wrong even after applying the techniques described in line 178-189. How will the invented predicates with wrong grounding affect downstream operator learning and planning? 
- I wonder how are the low-level skills realized in experiments for the proposed method and baselines. Are they realized by motion planning? Do you learn low-level skills in the MAPLE baseline?
- In Figure 4, it seems that the oracle baseline doesn't achieve 100% success rate in Blocks and Coffee domain. Its performance is even slightly worse than the proposed method in Blocks. This seems to be weird as the Blocks domain is the most well-known classical domain that can be solved completely with a few predicates and operators. I wonder why this happens?
Yes
No ethics review needed.
1. How does the framework ensure a robust and diverse predicate set while maintaining task relevance?
2. Is there a systematic mechanism for leveraging failures to refine or adjust future planning decisions, rather than simply discarding infeasible options?
1.	**Innovative Approach**: The paper introduces neuro-symbolic predicates to robot planning, using VLMs to invent predicates in a task-relevant manner, a strategy that has clear advantages over static symbolic approaches. The approach also supports perceptual abstraction and logical complexity, enabling the system to better generalize across environments.
2.	**Experimental Design**: The authors evaluate their approach across five different simulated robotic domains with various tasks, making the results generalizable. Furthermore, the comparisons with other SOTA, are well-chosen and highlight the advantages of the proposed method in terms of efficiency and adaptability.
3.	**Methodology and Algorithmic Design**: The online NSP invention algorithm is well-structured and incrementally builds task-relevant abstractions. By leveraging both VLMs for perceptual understanding and a symbolic component for logical manipulations, this approach improves sample efficiency, crucial in robotic applications where data is limited or expensive to obtain.
This paper proposes a novel framework for robot planning by integrating NSPs, a method that combines neural and symbolic representations. The authors present an algorithm for online predicate invention, leveraging VLMs to dynamically learn abstract world models through real-time interaction in various robotic environments. The study compares the proposed approach to several HRL and symbolic methods, evaluating performance in terms of sample efficiency, generalization, and interpretability.
1.	**Clarity of Explanation**: Some explanations in the paper are unclear. For example, in the paragraph beginning with line 178, the process of conditioning on the previous action should be explained in more detail. Additional details on how these predicate proposals are validated would improve the comprehensibility of the approach.
2.	**Reliance on VLMs and Practicality**: A potential limitation of this approach is its reliance on the accuracy of VLMs, which may be sensitive to changes in the environment. The approach assumes that perceptual cues relevant to task abstractions are identifiable through VLMs. However, variability in VLM performance, particularly with domain shifts or challenging visual inputs, can affect the system's robustness. Although the authors attempt to address this by conditioning predicates on past actions and object labeling, it is unclear how effective these strategies will be in practice, especially under less predictable real-world variations where perceptual noise could affect predicate accuracy.
3.	**Time Complexity and Performance**: As an online learning method, computational efficiency is critical, particularly for real-time applications in robotics. While the paper presents experimental results in supplementary materials, these do not convincingly demonstrate that the proposed approach maintains efficiency at scale. Including more detailed time complexity analysis or exploring algorithmic optimizations could address this concern, as current evaluations suggest that scalability in time-sensitive or resource-constrained environments may be limited. Moreover, the supplementary results lack a detailed breakdown, which would clarify how computational resources scale with task complexity.
learning neuro-symbolic predicates from interaction allows from improve sample efficiency, generalization and interpretability
@inproceedings{
anonymous2024online,
title={Online Neuro-Symbolic Predicate Invention for High-Level Planning},
author={Anonymous},
booktitle={Submitted to The Thirteenth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=QOfswj7hij},
note={under review}
}
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-language model 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.
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learning abstractions for planning neuro-symbolic ai concept learning
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neurosymbolic & hybrid AI systems (physics-informed, logic & formal reasoning, etc.)
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Online Neuro-Symbolic Predicate Invention for High-Level Planning
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