LLM Dynamic Planner (LLM-DP)

• LLM-DP is a planning system that leverages large language models to convert natural language instructions into hierarchical and adaptive plans.
• It uses few-shot in-context learning and logit biasing techniques to achieve high sample efficiency and robust decision-making in dynamic settings.
• The framework dynamically re-plans based on real-time sensory feedback, integrating high-level language understanding with low-level execution for adaptive performance.

A LLM Dynamic Planner (LLM-DP) is a class of planning system that leverages the reasoning, sample efficiency, and language understanding capabilities of LLMs to generate, adapt, and ground sequential plans for decision-making agents operating in dynamic, uncertain, or partially observable environments. The LLM-DP framework encompasses a range of approaches ranging from pure hierarchical LLM-driven architectures to hybrid neuro-symbolic frameworks that combine LLMs with symbolic planning, optimization, or domain-specific modules. These systems are characterized by their ability to decompose high-level natural language instructions into executable plans, replan adaptively in the face of environmental changes, and achieve strong generalization with limited supervised data.

1. Hierarchical and Modular Architectures

LLM-DP implementations commonly adopt a hierarchical planning paradigm, partitioning the overall procedure into a high-level planner and a low-level executor. The high-level planner (often an LLM such as GPT-3 or an equivalent) receives natural language instructions and constructs a high-level plan (HLP) represented as a sequence of abstract subgoals or action–object pairs. Each subgoal is subsequently mapped by a dedicated low-level planner to a sequence of atomic actions (navigation, manipulation, etc.) appropriate for the agent’s embodiment and environment (Song et al., 2022).

A representative architecture comprises the following modules:

• High-Level Planner (LLM): Generates HLPs and, crucially, supports dynamic grounded re-planning by incorporating contextual information extracted from the agent’s perceptual modules.
• Low-Level Planner/Executor: Operates at the resolution of primitive actions, such as moving, grasping, or interacting with objects, based solely on the current subgoal and updated environment state.
• Perception and Grounding Modules: Extract and inject structured scene information (e.g., observed objects, feasibility signals) to ensure physical alignment of plans with the actual environment.

This architecture enables conditional independence between language understanding and execution, mathematically formalized as:

$P(L_l \mid I, L_h, E) = P(L_l \mid L_h, E)$

where $L_l$ denotes the low-level action sequence, $I$ is the instruction, $L_h$ the high-level plan, and $E$ the environment state.

2. Few-Shot, Sample-Efficient Planning via In-Context Learning

A central tenet of LLM-DP approaches is the use of in-context learning to enable few-shot planning. Rather than requiring deep retraining on large numbers of task–plan pairs, the LLM is prompted at inference time with a carefully selected batch of demonstration pairs, often retrieved using a k-nearest-neighbor (kNN) approach over instruction embeddings (e.g., BERT). The prompt design typically features the explicit enumeration of valid high-level actions/objects and dynamic context, such as observed environment objects, to control generation diversity and reinforce grounding (Song et al., 2022).

Logit biasing strategies are also applied to favor generation of only admissible action–object pairs (e.g., through logit manipulation over allowed tokens), further improving planning accuracy while maintaining sample efficiency. Experimental evidence demonstrates that LLM-DP can achieve performance competitive with fully supervised baselines while using less than 0.5% of paired training data, an increase in sample efficiency by over two orders of magnitude.

3. Grounded Dynamic Re-Planning and Physical Adaptation

To function effectively in dynamic, partially observable, or open-world environments, LLM-DP frameworks implement closed-loop dynamic re-planning. Prompt updates—driven by feedback from onboard object detectors, feasibility modules, or failure signals—inject new observations and completed subgoals, ensuring that the LLM’s planning remains strictly grounded in the evolving state. The resulting system adapts the plan (or its continuation) on the fly, enabling robust recovery from dead ends, failed subgoals, or environmental changes encountered during execution.

A canonical algorithmic schema for dynamic grounded re-planning is as follows:

Algorithm: Dynamic Grounded Re-planning with LLM-Planner 1. Input: Instruction I, observed object list O, completed subgoals G 2. Generate high-level plan S = LLM(I, O, G) 3. Set subgoal index k = 0; extract s = S[k] 4. Execute low-level action for s 5. At each time step: a. Update O with new observations b. If s is not achieved (after n steps or repeated failures): Re-prompt LLM with latest I, O, G to regenerate S c. Else if s completed, increment k, set s = S[k] d. Continue low-level execution for s 6. Repeat until S is exhausted

This closed-loop architecture explicitly integrates planning and perception in a reactive feedback cycle, equipping the agent with the capability to re-plan high-level directives whenever the physical context changes or failures occur.

4. Empirical Performance and Analysis

LLM-DP methods have been evaluated on benchmarks such as ALFRED, a suite of long-horizon household tasks in simulated 3D visual environments. Key empirical findings include:

• Success Rates and Efficiency: LLM-DP integrated with strong baselines (e.g., HLSM) achieves goal-conditional success rates and planning accuracies comparable to or exceeding fully supervised baselines, despite drastically reduced data usage. Under few-shot constraints, LLM-DP consistently outperforms methods that retrain classical planners or use action-rank-only paradigms like SayCan.
• LLM Call Efficiency: Dynamic re-planning limits the number of LLM invocations (median ≈7 per ALFRED task) compared to over 20 for competitive ranking baselines.
• Ablation Studies: In-context retrieval, object-aware logit biasing, and prompt design are shown to substantially improve high-level planning accuracy. On high-complexity instructions, the closed-loop (dynamic) LLM-DP achieves superior task completion compared to static or non-grounded approaches.

5. Neuro-Symbolic and Hybrid Extensions

LLM-DP is extended in neuro-symbolic frameworks that combine LLMs with classical symbolic planners, belief state trackers, and action selectors (Dagan et al., 2023). In these, an LLM converts task instructions to PDDL (Planning Domain Definition Language) goals, while symbolic planners use world state and LLM-sampled beliefs to generate candidate plans, which are dynamically selected and executed. The closed-loop loop updates world state and uncertainties after each action. This setup is resilient to noisy observations and efficiently fuses sub-symbolic reasoning (LLM) with precise symbolic execution.

Mathematically, likely world states are sampled as:

$w_\text{belief} = W \cup \text{LLM}(B, W)$

and planning proceeds by solving:

$\pi = PG(w_\text{belief})$

This enables efficient decision-making under uncertainty and partial observability.

6. Theoretical Properties and Limitations

Theoretical analysis of LLM-DP in hierarchical reinforcement learning frameworks formalizes the in-context learning process as Bayesian Aggregated Imitation Learning (BAIL) (He et al., 30 May 2024). The LLM acts as a high-level planner in a partially observable Markov decision process (POMDP), aggregating expert demonstrations from its prompt into a probabilistic policy:

$$\pi_{h, \text{LLM}}(\cdot | \text{prompt}) = \sum_{z \in \mathcal{Z}} \pi_h^*(z | \text{history}) \cdot P(z | \text{prompt})$$

Critically, naive reliance on in-context LLM imitation can yield linear regret due to under-exploration. Introducing explicit exploration strategies (e.g., ε-greedy) achieves sublinear regret bounds:

$$\text{Reg}(T) \leq \widetilde{\mathcal{O}}(H^{3/2} \sqrt{T/\eta} \cdot \log(\cdot))$$

where $H$ is planning horizon and $\eta$ is distinguishability between latent models.

This analysis highlights that the balance between imitation and exploration, as well as quality of pretraining data, fundamentally governs LLM-DP performance guarantees.

7. Implications, Future Directions, and Applications

LLM-DP frameworks indicate a new path toward versatile, sample-efficient, and robust embodied agents and planning systems. Their condensation of commonsense knowledge in large pretrained models, dynamic physical grounding, and adaptability to changing instructions or environments position them as leading candidates for generalist agents, robotics, household automation, and other domains with complex, interactive planning requirements.

Future research is likely to focus on:

• Scaling to more sophisticated LLMs (such as Codex or multimodal variants)
• Enhanced prompt design and richer grounding through advanced perception or feedback
• Incorporation of refined symbolic or neuro-symbolic low-level planners for robust closed-loop integration
• Exploration-driven learning paradigms that harmonize imitation with strategic exploration in real-time environments

The LLM-DP paradigm, with its combination of in-context learning, hierarchical planning, and adaptive re-planning, establishes a foundation for flexible, human-interpretable, and highly efficient agents capable of tackling a diverse array of planning challenges under substantial environmental and informational uncertainty.