FATE: Closed-Loop Feasibility-Aware Task Generation with Active Repair for Physically Grounded Robotic Curricula

TL;DR

FATE (Feasibility-Aware Task gEneration), a closed-loop, self-correcting framework that reimagines task generation as an iterative validation-and-refinement process, and embeds a generalist embodied agent directly into the generation loop to proactively guarantee the physical groundedness of the resulting curriculum.

Abstract

Recent breakthroughs in generative simulation have harnessed Large Language Models (LLMs) to generate diverse robotic task curricula, yet these open-loop paradigms frequently produce linguistically coherent but physically infeasible goals, stemming from ungrounded task specifications or misaligned objective formulations. To address this critical limitation, we propose FATE (Feasibility-Aware Task gEneration), a closed-loop, self-correcting framework that reimagines task generation as an iterative validation-and-refinement process. Unlike conventional methods that decouple generation and verification into discrete stages, FATE embeds a generalist embodied agent directly into the generation loop to proactively guarantee the physical groundedness of the resulting curriculum. FATE instantiates a sequential auditing pipeline: it first validates static scene attributes (e.g., object affordances, layout compatibility) and subsequently verifies execution feasibility via simulated embodied interaction. Critical to its performance, upon detecting an infeasible task, FATE deploys an active repair module that autonomously adapts scene configurations or policy specifications, converting unworkable proposals into physically valid task instances. Extensive experiments validate that FATE generates semantically diverse, physically grounded task curricula while achieving a substantial reduction in execution failure rates relative to state-of-the-art generative baselines.

Figures (4)

• Figure 1: FATE aligns task feasibility across static and dynamic dimensions.(Left) Static Alignment: The system corrects initial scene priors by resolving semantic violations (e.g., replacing raw eggs), geometric chaos, and precondition conflicts. (Right) Dynamic Alignment: FATE addresses runtime execution failures, such as high-torque grasps, unreachable targets, and collisions, through iterative rollback mechanisms. Red highlights indicate initial infeasibilities; Green indicates FATE-repaired executable environments.
• Figure 2: Visualization of the Hierarchical Feasibility Alignment Process. The optimization trajectory evolves in the joint task space $\tau = (\mathcal{I}, \mathcal{S}, \Pi)$. (Left) Static Alignment ($\mathcal{A}_{perc}$): The Ante-Auditor corrects Scene ($\mathcal{S}$) and Instruction ($\mathcal{I}$) incompatibilities via discrete repairs (e.g., RESCALE), projecting $\tau_{init}$ onto the perceptually valid manifold. (Right) Dynamic Alignment ($\mathcal{A}_{exec}$): Targeting the Policy ($\Pi$), the system refines the solver configuration using semantic feedback and dynamic APIs (e.g., INJECT REWARD), guiding the task from failure (red trajectories) to the Feasible Space (green polyhedron).
• Figure 3: Evolutionary Trajectory of the "Heat Food" Task. Starting from a chaotic initialization ($\tau_{init}$), FATE applies Static Repair to rearrange objects and correct affordances (swapping an egg for a meal). During execution, the system autonomously detects and repairs dynamic failures: a grasp failure is resolved via Motion Re-planning, and an insertion collision is fixed via Asset Resizing. The gray curved arrows illustrate our Sub-step Rollback mechanism, which invokes specific repair APIs (bottom icons) to iteratively refine the task into a successful policy.
• Figure 4: Task-Wise Success Rate Analysis. We evaluate performance across five tasks to disentangle the contributions of the alignment operators. The Static Alignment ($\mathcal{A}_{perc}$) module excels in geometry-sensitive tasks (e.g., Ingredient into Pot), while the Dynamic Alignment ($\mathcal{A}_{exec}$) module is critical for contact-rich tasks (e.g., Sliding Window). FATE (Green) combines both to achieve robust performance.

Theorems & Definitions (8)

• Definition 3.1: Feasibility-Aware Task
• Definition 3.2: Feasible Policy Set
• Definition 3.3: Task Feasibility Measure
• Definition 3.4: Feasibility Gap
• Proposition 4.1: Convergence of Hierarchical Alignment
• Proposition 1.4: Linear Convergence of Iterative Repair
• proof
• Lemma 2.1: Static Necessity