Planning Task Shielding: Detecting and Repairing Flaws in Planning Tasks through Turning them Unsolvable
- URL: http://arxiv.org/abs/2604.07042v1
- Date: Wed, 08 Apr 2026 12:57:37 GMT
- Title: Planning Task Shielding: Detecting and Repairing Flaws in Planning Tasks through Turning them Unsolvable
- Abstract summary: We introduce planning task shielding: the problem of detecting and repairing flaws in planning tasks.<n>We propose $allmin$, an optimal algorithm that solves these tasks by minimally modifying the original actions.<n>We empirically evaluate the performance of $allmin$ in shielding planning tasks of increasing size.
- Score: 5.343135467323389
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Most research in planning focuses on generating a plan to achieve a desired set of goals. However, a goal specification can also be used to encode a property that should never hold, allowing a planner to identify a trace that would reach a flawed state. In such cases, the objective may shift to modifying the planning task to ensure that the flawed state is never reached-in other words, to make the planning task unsolvable. In this paper we introduce planning task shielding: the problem of detecting and repairing flaws in planning tasks. We propose $allmin$, an optimal algorithm that solves these tasks by minimally modifying the original actions to render the planning task unsolvable. We empirically evaluate the performance of $allmin$ in shielding planning tasks of increasing size, showing how it can effectively shield the system by turning the planning task unsolvable.
Related papers
- PlanningBench: Generating Scalable and Verifiable Planning Data for Evaluating and Training Large Language Models [52.48858778580074]
Planning is a fundamental capability for large language models (LLMs)<n>PlanningBench is a framework for generating scalable, diverse, verifiable planning data for both evaluation and training.
arXiv Detail & Related papers (2026-05-20T08:10:15Z) - From Plan to Action: How Well Do Agents Follow the Plan? [6.54753809716901]
This paper presents the first systematic analysis of plan compliance in programming agents.<n>We examined 16,991 trajectories from SWE-agents across four LLMs on SWE-bench Verified and SWEbench Pro under eight plan variations.<n>A subpar plan hurts performance even more than no plan at all.
arXiv Detail & Related papers (2026-04-13T23:54:55Z) - Effective Task Planning with Missing Objects using Learning-Informed Object Search [12.369687852088859]
We develop a planning framework centered around novel model-based LIOS actions.<n>High-level planning treats LIOS actions as deterministic and so -- informed by model-based calculations of the expected cost of each -- generates plans that interleave search and execution.<n>In simulated ProcTHOR homes and in the real world, our approach outperforms non-learned and learned baselines on tasks including retrieval and meal prep.
arXiv Detail & Related papers (2026-02-12T00:56:35Z) - Planning with Minimal Disruption [9.722824469961925]
In many planning applications, we might be interested in finding plans that minimally modify the initial state to achieve the goals.<n>In this paper, we formally introduce it, and define various planning-based compilations that aim to jointly optimize both the sum of action costs and plan disruption.
arXiv Detail & Related papers (2025-08-21T08:38:17Z) - A Planning Compilation to Reason about Goal Achievement at Planning Time [9.722824469961925]
We propose a compilation that extends the original planning task with commit actions that enforce the persistence of specific goals once achieved.<n> Experimental results indicate that solving the reformulated tasks does not incur on any additional overhead both when performing optimal and suboptimal planning.
arXiv Detail & Related papers (2025-03-12T17:00:37Z) - Hindsight Planner: A Closed-Loop Few-Shot Planner for Embodied Instruction Following [62.10809033451526]
This work focuses on building a task planner for Embodied Instruction Following (EIF) using Large Language Models (LLMs)<n>We frame the task as a Partially Observable Markov Decision Process (POMDP) and aim to develop a robust planner under a few-shot assumption.<n>Our experiments on the ALFRED dataset indicate that our planner achieves competitive performance under a few-shot assumption.
arXiv Detail & Related papers (2024-12-27T10:05:45Z) - Unified Task and Motion Planning using Object-centric Abstractions of
Motion Constraints [56.283944756315066]
We propose an alternative TAMP approach that unifies task and motion planning into a single search.
Our approach is based on an object-centric abstraction of motion constraints that permits leveraging the computational efficiency of off-the-shelf AI search to yield physically feasible plans.
arXiv Detail & Related papers (2023-12-29T14:00:20Z) - Learning adaptive planning representations with natural language
guidance [90.24449752926866]
This paper describes Ada, a framework for automatically constructing task-specific planning representations.
Ada interactively learns a library of planner-compatible high-level action abstractions and low-level controllers adapted to a particular domain of planning tasks.
arXiv Detail & Related papers (2023-12-13T23:35:31Z) - Planning as In-Painting: A Diffusion-Based Embodied Task Planning
Framework for Environments under Uncertainty [56.30846158280031]
Task planning for embodied AI has been one of the most challenging problems.
We propose a task-agnostic method named 'planning as in-painting'
The proposed framework achieves promising performances in various embodied AI tasks.
arXiv Detail & Related papers (2023-12-02T10:07:17Z) - Sequence-Based Plan Feasibility Prediction for Efficient Task and Motion
Planning [36.300564378022315]
We present a learning-enabled Task and Motion Planning (TAMP) algorithm for solving mobile manipulation problems in environments with many articulated and movable obstacles.
The core of our algorithm is PIGINet, a novel Transformer-based learning method that takes in a task plan, the goal, and the initial state, and predicts the probability of finding motion trajectories associated with the task plan.
arXiv Detail & Related papers (2022-11-03T04:12:04Z) - Task Scoping: Generating Task-Specific Abstractions for Planning [19.411900372400183]
Planning to solve any specific task using an open-scope world model is computationally intractable.
We propose task scoping: a method that exploits knowledge of the initial condition, goal condition, and transition-dynamics structure of a task.
We prove that task scoping never deletes relevant factors or actions, characterize its computational complexity, and characterize the planning problems for which it is especially useful.
arXiv Detail & Related papers (2020-10-17T21:19:25Z) - Divide-and-Conquer Monte Carlo Tree Search For Goal-Directed Planning [78.65083326918351]
We consider alternatives to an implicit sequential planning assumption.
We propose Divide-and-Conquer Monte Carlo Tree Search (DC-MCTS) for approximating the optimal plan.
We show that this algorithmic flexibility over planning order leads to improved results in navigation tasks in grid-worlds.
arXiv Detail & Related papers (2020-04-23T18:08:58Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.