Context Matters! Relaxing Goals with LLMs for Feasible 3D Scene Planning (2506.15828v1)

Abstract: Classical planning in AI and Robotics addresses complex tasks by shifting from imperative to declarative approaches (e.g., PDDL). However, these methods often fail in real scenarios due to limited robot perception and the need to ground perceptions to planning predicates. This often results in heavily hard-coded behaviors that struggle to adapt, even with scenarios where goals can be achieved through relaxed planning. Meanwhile, LLMs lead to planning systems that leverage commonsense reasoning but often at the cost of generating unfeasible and/or unsafe plans. To address these limitations, we present an approach integrating classical planning with LLMs, leveraging their ability to extract commonsense knowledge and ground actions. We propose a hierarchical formulation that enables robots to make unfeasible tasks tractable by defining functionally equivalent goals through gradual relaxation. This mechanism supports partial achievement of the intended objective, suited to the agent's specific context. Our method demonstrates its ability to adapt and execute tasks effectively within environments modeled using 3D Scene Graphs through comprehensive qualitative and quantitative evaluations. We also show how this method succeeds in complex scenarios where other benchmark methods are more likely to fail. Code, dataset, and additional material are released to the community.

Analysis of "Context Matters! Relaxing Goals with LLMs for Feasible 3D Scene Planning"

The integration of classical AI planning methodologies with LLMs constitutes a novel approach in scene planning, as presented by the authors Emanuele Musumeci et al. Their paper, titled "Context Matters! Relaxing Goals with LLMs for Feasible 3D Scene Planning," offers a significant contribution to the domain of robotics and AI, particularly concerning task execution in real-world environments modeled using 3D Scene Graphs.

Overview and Methodology

At its core, the paper addresses the limitations inherent in classical planning when applied to real-world robotic scenarios, where plans often fail due to incomplete perception groundings and the inflexibility of static goal definitions. The authors propose an innovative solution by integrating LLMs—known for their commonsense reasoning capabilities—into the planning process. This integration is operationalized through a bi-dimensional framework: situational shifting and goal relaxation.

The situational shifting operator adapts the planning environment representation by progressively adjusting the domain specification using LLM-driven reasoning based on scene semantics. Concurrently, goal relaxation provides a hierarchical mechanism to reduce constraints, ensuring functionally equivalent but more contextually feasible goals. These operators work synergistically to navigate through a relaxation graph that represents a spectrum of planning problems. This dual strategy advances the appropriateness and adaptability of planning in dynamic settings.

Experimental Evaluation

The authors support their methodology with extensive experiments using an augmented dataset of complex household tasks and scenes described by 3D Scene Graphs. Notably, their approach achieves a commendable success rate, particularly when the grounding of plans is meticulously checked against real environmental data, attesting to the framework's robustness. The dataset, extended with additional objects to challenge the planning process, forms a critical resource for benchmarking and is made publicly available.

Comparisons and Limitations

In relation to existing state-of-the-art methodologies, particularly DELTA, the presented approach demonstrates superior adaptability and plan feasibility. DELTA's focus on converting natural language tasks to PDDL with LLMs falls short when domain environments are incompletely modeled or impractical to align directly with plan executions. The paper accentuates the pre-grounding plan evaluation step, incorporating feedback for logical coherence, thereby significantly improving the success rate.

Nevertheless, the paper acknowledges inherent limitations, particularly regarding unfeasible task identification where LLMs may attempt exhaustive solvation. A suggested future avenue involves augmenting the relaxation graph exploration mechanism to improve the framework's efficacy in discerning inherently impossible goals.

Theoretical and Practical Implications

This research harbors significant implications for both the theoretical understanding and practical application of AI in robotics. The bi-dimension task adaptation framework enriches the theoretical model of AI planning by integrating semantic flexibility and hierarchical goal handling. Practically, this methodology underscores the utility of LLMs beyond mere linguistic capabilities, positioning them as instrumental in real-time, cognitive robotic planning.

Moving forward, the research advocates for utilizing FMs' contextual handling capacities across diverse, dynamically changing, real-world settings. Emphasis on improving grounding checks and adapting the task representation in semantics-driven planning may catalyze emerging AI models' adoption in more complex robotics applications.

In conclusion, "Context Matters!" successfully illustrates an innovative approach, merging advanced linguistic processing systems with AI planning strategies to accommodate the intrinsic unpredictability of robotic environments. The groundwork laid in this paper creatively juxtaposes classical and modern computational methodologies, promising to reshape future advancements in interactive and autonomous robotics systems.