Feasibility-Guided Planning Framework

• Feasibility-guided planning frameworks are structured methodologies that integrate early feasibility checks as primary constraints to ensure that generated plans are both goal-relevant and executable.
• They employ hierarchical pipelines and interleaved evaluations using data-driven models and formal methods to prune infeasible steps and improve planning efficiency.
• By combining learned feasibility models with formal constraint checks, these frameworks achieve significant gains in success rates and computational speed across diverse domains.

A feasibility-guided planning framework is a structured, algorithmic methodology that integrates feasibility considerations as primary constraints or guiding heuristics throughout the planning process, ensuring that generated solutions are not only goal-relevant but are practically executable given physical, logical, or operational constraints. This paradigm appears across robotics, control, AI planning, and domain-specific applications such as synthesis planning and energy system design, and is characterized by the explicit, early, and adaptive enforcement or estimation of feasibility at each decision stage, often interleaved with or prior to optimality- or reward-based objectives. Multiple research efforts exemplify this principle across diverse planning modalities, incorporating both data-driven models (e.g., neural networks, LLMs) and formal methods (e.g., symbolic logic, trajectory optimization) (Chen et al., 12 Dec 2025, Kwon et al., 30 Oct 2025, Xuan-Vu et al., 18 Dec 2025, Attali et al., 2024, Luo et al., 8 Feb 2026, Ly et al., 2023, Zhang et al., 12 Jul 2025, Tuncer et al., 3 Nov 2025).

1. Foundations and Theoretical Formulation

A feasibility-guided planning framework formalizes the problem as the search for sequences of actions or states, subject to a set of hard constraints that encode feasibility. These constraints may involve collision-freeness, resource and time-window satisfaction, or logical consistency, depending on application domain.

In motion planning, the configuration space $C$ admits a free subset $C_{\mathrm{free}} \subset C$ defined by the feasibility of configurations—typically as collision-free or dynamically permissible regions (Attali et al., 2024). Feasibility is encoded as a probability $p_{\mathrm{free}}(q)$ or a hard predicate $F(q)$, guiding heuristic search or probabilistically weighted sampling.

Task and motion planning (TAMP) frameworks define hybrid state spaces $H = S \times X$ (symbolic $\times$ geometric), and ensure that each action’s discrete effect is consistent with its low-level, physically feasible execution, often verified through sampling-based planners and/or physics simulators (Kwon et al., 30 Oct 2025, Ly et al., 2023).

In sequential manipulation and rearrangement (Tuncer et al., 3 Nov 2025), feasibility constraints manifest as pick-and-place geometric checks, critical obstruction detection, and the requirement that inter-object dependencies (e.g., no premature occlusion) are obeyed at every planning step.

In formally constrained domains (e.g., trip planning (Chen et al., 12 Dec 2025), energy system design (Silva et al., 2020)), the feasible set $F$ is constructed as a region in parameter space defined by explicit inequalities representing technical, economic, or temporal requirements.

2. Architecture and Workflow Patterns

Frameworks universally employ multi-stage pipelines where feasibility is checked, enforced, or used to prune and guide at each major stage:

• Filtering and Pruning: Early rejection of objects, subgoals, candidate actions, or plans that violate basic constraints (e.g., unreachable destinations, missing data, collision risks) vastly reduces downstream computational cost and residual infeasibilities (Chen et al., 12 Dec 2025, Tuncer et al., 3 Nov 2025, Kwon et al., 30 Oct 2025).
• Hierarchical and Interleaved Evaluation: High-level symbolic planning produces candidate plans which are then filtered or scored according to feasibility criteria, either via rapid learned heuristics (e.g., neural feasibility classifiers (Xu et al., 2022)) or via formal predicate simulation (Tang et al., 25 Jan 2025, Ly et al., 2023). In kinodynamic TAMP (Kwon et al., 30 Oct 2025), geometric and dynamic feasibility checks are strictly interleaved with symbolic expansion.
• Feedback and Refinement: Feasibility-guided frameworks actively detect and correct constraint violations (e.g., in “governance” stages (Chen et al., 12 Dec 2025) or through bounded iterative refinement). In chemical synthesis (Xuan-Vu et al., 18 Dec 2025), self-evaluation loops feed back LLM-derived feasibility critiques to steer replanning toward more executable strategies.
• Feasibility Models and Surrogates: Where full constraint checking is costly (e.g., inverse kinematics or dynamic simulation), learned surrogates—such as Feasibility-Nets mapping environmental observations to per-action feasibility tensors—enable fast, scalable online reasoning across large state spaces (Luo et al., 8 Feb 2026, Xu et al., 2022).
• Pseudocode Exemplar (Chen et al., 12 Dec 2025):

  def PLAN(retrieved_set, preferences): itinerary = initialize_skeleton(retrieved_set) for module in [transportation, lodging, meals, activities]: while True: suggestion = LLM_suggest(module, context=itinerary, preferences) if validator(module, suggestion, itinerary) == PASS: itinerary = integrate(module, suggestion) break return itinerary

  Here, the validator enforces monotonic feasibility—ensuring no constraint is violated by any suggestion.

3. Feasibility Guidance Mechanisms

Feasibility-guided methods instantiate several explicit strategies:

• Guiding Space/Distribution: Define a guiding distribution $$p_G(q) \propto p_{\mathrm{free}}(q)^\alpha p_{\mathrm{prior}}(q)^{1-\alpha}$$, combining feasibility scores and additional heuristics, for biased sampling or expansion in sampling-based planners (Attali et al., 2024).
• Reachability Graphs and Heuristics: Precompute or incrementally sample and connect feasible waypoints, forming a reachability graph containing only feasible transitions. Heuristics derived from these graphs guide abstract/symbolic search layers and provide collision-free skeletons for low-level optimization (Ly et al., 2023).
• Feasibility Tensors and Policy Fusion: In multi-expert control, each policy is assigned a dedicated feasibility-net predicting success probabilities given local terrain and command. These are fused into cost-maps, enabling classical planners (e.g., Dijkstra) to generate globally feasible paths that adhere to the capabilities of each underlying policy (Luo et al., 8 Feb 2026).
• Dynamic Feasibility and Projection: Differentiable hard-constraint projection (e.g., via adaptive neural projections (Wang et al., 27 Jan 2026)) ensures that even learned or approximate policy outputs are projected onto the exact feasible set (equality constraints), with the projection implemented as a differentiable Newton-type iteration.
• Feedback-Driven Self-Correction: In domains with high uncertainty or partial observability, feasibility models are actively improved via active learning or agent self-evaluation (e.g., Bayesian active learning for abstract-plan feasibility (Noseworthy et al., 2021), or LLM self-evaluation in chemical synthesis (Xuan-Vu et al., 18 Dec 2025)).

4. Domain-Specific Implementations and Quantitative Results

Feasibility-guided frameworks have been successfully instantiated in numerous domains, each reporting substantial improvements in execution success rate, planning efficiency, or both:

Research Area	Framework/Method	Notable Results	Reference
Trip/Itinerary Planning	TriFlow	91.1–97.7% final pass, ~10× faster than SOTA, macro feasibility 99.1%	(Chen et al., 12 Dec 2025)
Kinodynamic TAMP	VLM-guided TAMP	92.5–95% SR, up to 23× SR gain over no-feasibility baseline	(Kwon et al., 30 Oct 2025)
Chemical Synthesis	Synthelite	95% solve rate, median feasibility 8/10, >75% routes ≥7/10 (expert LLM-judge)	(Xuan-Vu et al., 18 Dec 2025)
Locomotion/Control	Feasibility-Net (Multi-Policy)	98.6–100% SR on mixed terrains, robust policy-switching success rates	(Luo et al., 8 Feb 2026)
Mobile Manipulation	R-LGP (Reachability-LGP)	100% SR, 24× speedup in sorting, guaranteed collision-free	(Ly et al., 2023)
Rearrangement Planning	MO-SeGMan	Always feasible, sublinear scaling in obstacle count, outperforms baselines	(Tuncer et al., 3 Nov 2025)
Embedded Path Planning	AdaNP Hard Projection	88.75% SR, 94 ms latency, strong kinematic compliance on resource-constrained HW	(Wang et al., 27 Jan 2026)

The consistent theme is that explicit, early, and/or continuous feasibility checking dramatically increases both the reliability and efficiency of planners, often providing strong theoretical guarantees (e.g., probabilistic completeness (Kwon et al., 30 Oct 2025), asymptotic stability (Zhang et al., 12 Jul 2025)) in domains previously limited by heuristic or nonconstructive approaches.

5. Information-Theoretic and Complexity Perspectives

Some frameworks employ information-theoretic metrics (e.g., Kullback–Leibler divergence) to quantify the match between actual sampling distributions and those optimizing for feasibility and optimality (Attali et al., 2024). Sampling efficiency metrics formally assess how well guidance mechanisms concentrate computational effort in feasible, reward-relevant regions.

Hybrid and multi-objective planners often blend feasibility guidance with goal-directed or utility-based heuristics, employing mixture models or online-adaptive weighting schemes to trade off between strict feasibility and exploration for optimality (Attali et al., 2024, Tuncer et al., 3 Nov 2025).

From a complexity perspective, substantial computational savings are achieved by pruning infeasible branches before engaging in expensive, downstream search or optimization (e.g., motion planning, trajectory optimization), and by modularizing feasibility estimation for incremental extension and robustness (e.g., when adding new skills or policies (Luo et al., 8 Feb 2026)).

6. Extensions, Limitations, and Future Directions

Feasibility-guided frameworks are extensible across multiple modalities:

• Active Feasibility Model Learning: Frameworks increasingly employ active, experiment-driven learning to rapidly adapt feasibility models to new domains, configurations, or hardware perturbations, improving out-of-distribution generalization (Noseworthy et al., 2021).
• Integration with Learning-based Policies: Differentiable feasibility projections and hard-constraint architectures allow neural policies to meet operational safety and kinematic constraints in real-time, enhancing their deployability in safety-critical settings (Wang et al., 27 Jan 2026).
• Human-in-the-Loop Governance: Self-evaluation, LLM judgment, and user-driven prompts allow feasibility modules to reflect both hard, codified constraints, and expert domain knowledge (Xuan-Vu et al., 18 Dec 2025).

Limitations include the dependence of some models on the coverage and realism of training data (e.g., Feasibility-Net OOD detection (Luo et al., 8 Feb 2026)), assumptions of zero switching cost or negligible transition overhead (policy selection for rapid mode switching), and the increased offline computation needed for reachability graph construction in large-scale or highly dynamic environments.

A plausible implication is that, as planning domains grow in size and complexity, scalable feasibility-guided methodologies—combining formal guarantees, learned surrogates, and modular, adaptive architectures—will play a central role in bridging the gap between theoretical planning and real-world, trustworthy deployment.

7. Cross-Domain Synthesis and Outlook

While feasibility-guided planning originated in robotics and motion planning, the same principles are now foundational in areas as diverse as autonomous navigation, multi-modal trip planning, chemical synthesis design, and even techno-economic optimization for renewable energy systems (Silva et al., 2020). These frameworks formalize feasibility as a primary, structuring constraint—mathematically, algorithmically, and heuristically—and provide the foundation for robust, efficient, and trustworthy planning in physical and digital systems. The universal structure across domains is early, explicit, and adaptive feasibility guidance, supporting both generalization to novel scenarios and the strong guarantees required for safe, optimal operation.