Zero-Shot Task Execution

• Zero-shot task execution is an AI paradigm that maps novel task instructions to executable plans using pre-trained models and meta-learning, without any labeled data.
• It leverages LLMs, vision-language models, and reward-based encoders to achieve rapid adaptation and robust performance in NLP, robotics, and reinforcement learning.
• Empirical benchmarks demonstrate competitive success rates and reveal challenges in scalability and generalization, guiding future innovations in autonomous planning.

Zero-shot task execution refers to the automated completion of novel tasks—defined by instructions, goals, rewards, or logical formulas—without any labeled data, demonstrations, or environment-specific fine-tuning for those particular tasks. Methods for zero-shot execution leverage pre-existing models, representations, or meta-learning strategies to ground new task specifications directly into executable plans or policies. This paradigm is foundational to building generalist agents in NLP, robotics, and reinforcement learning that are capable of rapid, versatile adaptation to unseen instructions and environments.

1. Principles and Formal Problem Settings

In zero-shot task execution, the agent is expected to map a task specification (instruction, goal image, reward function, logical formula, etc.) to a sequence of low-level actions that achieve the task, without access to task-specific training data. Formally, given a task description $I$ and a set of constraints or context $C$, the agent seeks a mapping

$f: I \times C \longrightarrow A$

where $A$ is a grounded and executable sequence of actions (or skill invocations), subject to constraints on action transitions and task success criteria (Lin et al., 4 Mar 2025).

Distinct formalizations occur across domains:

• Instruction following: The agent must transform a natural language instruction into an action sequence achieving a goal under operational and logical constraints (Lin et al., 4 Mar 2025).
• Reward/goal-based RL: Given a new reward function $r$, the agent must compute a policy $\pi_r$ instantaneously, exploiting some learned environment summarization but no task-labeled data (Ollivier, 15 Feb 2025, Touati et al., 2022).
• Task adaptation/meta-mapping: The agent learns a shared latent space of task representations such that meta-transformations (e.g., “invert objective”, “try to lose instead of win”) can be composed to yield new behaviors without further training (Lampinen et al., 2019).
• Grasping and manipulation: Object-centric goal or affordance descriptions (text, image, mask) are mapped to a manipulation policy in a zero-shot fashion (Holomjova et al., 5 Jun 2025, Cui et al., 2022).

Typical constraints involve operational feasibility (no forbidden transitions), success in achieving the (possibly natural-language or logic-defined) goal, and sometimes explicit cost or path-length metrics (Lin et al., 4 Mar 2025).

2. Foundational Methodologies

Zero-shot execution mechanisms are diverse, but most can be grouped into several methodological families:

• LLM-Based Multi-Stage Planning: Structured pipelines use LLMs in modular stages—task template retrieval, chain-of-thought decomposition, symbolic parsing, and logical/constraint-checking—to generate valid action sequences from free-form user instructions, operationalized for environments such as AI2-THOR and real robot control (Lin et al., 4 Mar 2025). This modularity is critical; ablations show that excluding reasoning or logical evaluation causes significant (>30–50%) drops in success rate.
• Zero-Shot Recognition and Grounding: Vision-LLMs (CLIP, SAM) enable object, region, or affordance grounding from textual prompts (“red screwdriver with black handle”) without any visual references, facilitating zero-shot object selection for grasping and manipulation. Embedding-based similarity enables robust multi-object selection and generalization to previously unseen categories, trading off modest accuracy loss for annotation-free scalability (Holomjova et al., 5 Jun 2025).
• Task Representation Learning: Agents build shared latent spaces for task definitions such that embeddings for data, tasks, and even meta-transformations are mutually transformable. Homoiconic meta-mapping explicitly learns mappings not just from data to tasks but from tasks to tasks, supporting rapid zero-shot adaptation to novel task transformations (e.g., “try to lose at chess”) (Lampinen et al., 2019).
• Reward- and Function-Based Encodings: Universal policy architectures represent tasks either via explicit reward functions (Ollivier, 15 Feb 2025), basis-function encodings (Ingebrand et al., 2024), or "successor features" such that, given a new reward description or goal cell, the agent instantaneously computes a sufficient embedding or policy parameter without further gradient descent, planning, or environment interaction (Touati et al., 2022).
• Compositional Skill Decomposition: Long-horizon manipulation tasks are decomposed into atomic skills, each self-contained and learnable with dense supervision, then recomposed at inference time by sequencing through VLM-guided parsing of novel high-level instructions. Collision-free skill chaining is achieved by spatial planners to ensure robust physical execution (Chen et al., 1 May 2025).
• Logical and Compositional Networks: For logic-defined tasks (e.g. LTL formulas), network architectures literally encode the formula’s parse tree as a composition of trainable modules (one per logic operator), supporting true zero-shot generalization to never-seen formulas by structural induction (Kuo et al., 2020).

3. Representative Benchmarks and Empirical Results

Benchmarks span knowledge domains, complexity classes, and embodiment:

• Instruction-Following in Embodied Environments: FlowPlan achieves a zero-shot success rate of 35.64–40.31% on ALFRED test sets, approaching (within ~5–9%) in-context learning baselines—without any labeled examples or data-driven prompt engineering—by decomposing reasoning, planning, and validation across four LLM-driven stages (Lin et al., 4 Mar 2025). Logical evaluation and explicit language-level reasoning are essential for competitive performance.
• Grasping and Manipulation: Binary-TOG achieves 68.9% task-oriented grasp accuracy in multi-object scenes using only zero-shot textual prompts for target object selection, compared to 70.8% for a one-shot visual reference baseline and 52.3% for standard non-KB methods (Holomjova et al., 5 Jun 2025).
• NLP Task Generalization: Prompt consistency regularization (swarm distillation) boosts zero-shot ensemble accuracy by up to +10.6 points on language tasks (e.g., NLI, story cloze, COPA), sometimes with as few as 10 unlabeled examples and 4 human-written prompts—a significant gain over T0 baseline (Zhou et al., 2022).
• Zero-Shot RL in Continuous Control: With learned representations based on forward-backward (FB) features, agents attain ~81% of supervised RL performance across four continuous-control benchmarks (maze, walker, cheetah, quadruped) using only a reward-free offline buffer and a single regression step from new reward to policy (Touati et al., 2022). Successor features with Laplacian embeddings trail slightly at 74%.
• Robotic Sim-to-Real Transfer: Visuomotor skills and learned logical predicate models (via depth + PointNet++) enable >95% stacking success in simulation and 80% in real-world 4-block stacking—without any fine-tuning on real images—by leveraging mask-based state abstraction and reactive STRIPS-style planning (Mukherjee et al., 2020).
• Task Adaptation via Meta-Learning: Homoiconic meta-mapping achieves held-out polynomial regression MSE ≪1 and out-of-domain task remapping reward ≈0.3–0.5, far exceeding language-only transfer in polynomial and card-game environments (Lampinen et al., 2019).

4. Mathematical Foundations and Constraints

Many zero-shot methods instantiate a mapping from structured input (natural language, reward function, logic formula) to compact representations or full action sequences subject to multiple classes of constraints:

• Operational Constraints: Sequences of primitives must avoid forbidden transitions, often formalized as hard logical constraints or cost penalties $\sum_{n} \delta_C(A_n, A_{n+1})$ (Lin et al., 4 Mar 2025).
• Goal Achievement: Success predicates encode high-level task completion (e.g., all goal conditions in instruction $G(A) = 1$) (Lin et al., 4 Mar 2025).
• Feasibility and Validity: Logical evaluation modules and verification stages ensure that plans respect required preconditions, and contextual grounding aligns symbolic steps to observed instance-level objects (Lin et al., 4 Mar 2025, Chen et al., 1 May 2025).

Representative formulations include:

• Latent-space planning: $f(I;C) = A$, subject to constraint_violation and success predicates (Lin et al., 4 Mar 2025).
• Projection-based RL: For reward $r$, compute $$z = (Cov_\rho[\phi, \phi])^{-1} E_{s\sim\rho}[r(s) \phi(s)]$$, then policy $\pi_z$ (Ollivier, 15 Feb 2025, Touati et al., 2022).
• Meta-mapping: Transform a function embedding $z^{func}$ via meta-mapping network $F_{z^{meta}}$ to produce zero-shot task embeddings (Lampinen et al., 2019).

5. Modalities and Task Specification Interfaces

Zero-shot execution leverages richly multi-modal interfaces:

• Natural Language: LLM pipelines convert instructions to action sequences and symbolic plans (Lin et al., 4 Mar 2025, Li et al., 2023), and zero-shot object recognition via language prompts supports rapid extensibility (Holomjova et al., 5 Jun 2025).
• Visual and Semantic Descriptions: Internet images, user sketches, and text labels are embedded via foundation models (e.g., CLIP) to define goal states or desired scene deltas, with similarity-based scoring driving goal detection and even serving as reward proxies in offline RL (Cui et al., 2022).
• Logical Formulas and Programmatic Tasks: Parsing-based, compositional network architectures handle the inductive generalization needed for logical-task specification (e.g., LTL), supporting robust zero-shot execution via grammar-directed network assembly (Kuo et al., 2020).
• Reward Functions and State-Action Statistics: Universal RL approaches transform entire classes of reward functions (including dense, sparse, and temporally smooth priors) into low-dimensional encodings for instant policy computation (Ollivier, 15 Feb 2025, Touati et al., 2022, Ingebrand et al., 2024).

6. Empirical Limitations and Scaling Characteristics

Current zero-shot task execution frameworks exhibit both concrete successes and domain-dependent limitations:

• LLM-based planning pipelines: Zero-shot performance remains ~5–10pp below "expert-trace" or in-context learning baselines in complex, unconstrained embodied domains; incomplete reasoning or weak logical checks reduce SR by >30–50% (Lin et al., 4 Mar 2025).
• Visual foundation model limitations: Zero-shot recognition accuracy degrades on rare-object or fine subcategory discriminations, with sensitivity to prompt phrasing and segmentation errors (Holomjova et al., 5 Jun 2025). Video-domain gaps can sharply reduce object embedding reliability (Cui et al., 2022).
• Reward-based RL methods: Coverage is limited by the span of the learned representations; with feature collapse or coverage gaps, only tasks within the learned subspace can be solved optimally (Touati et al., 2022, Ollivier, 15 Feb 2025, Ingebrand et al., 2024). Empirical success also depends on the richness of the unsupervised/replay buffer and the structural priors (e.g., Laplacian eigenfunctions for SFs).
• Meta-mapping and transfer: Generalization depends critically on the diversity of meta-mappings seen during training; models degrade for out-of-distribution transformations or insufficiently expressive latent spaces (Lampinen et al., 2019).

7. Future Directions and Open Challenges

Zero-shot task execution remains an area of active research, with multiple challenges outstanding:

• Task Specification Robustness: Making pipelines more resilient to ambiguities, rare edge cases, and complex compositional instructions—potentially by integrating more structured symbolic or probabilistic reasoning layers.
• Representation Capacity: Enabling representations to scale smoothly to higher-dimensional, real-world domains, and ensuring they cover both globally distributed and fine-grained, local reward or goal structures.
• Generalization Beyond Linear Span: Extending function-encoder and universal RL approaches to non-linear, distributionally-shifted, or programmatically-defined task families.
• Continual and Online Learning: Hybrid approaches that blend zero-shot adaptation with efficient few-shot or online updating, possibly integrating self-supervised data collection or auxiliary online calibration.
• Closed-Loop and Reactive Execution: Greater integration of perception-action feedback, error recovery, and re-planning in the face of failure or environmental drift.
• Benchmarking and Standardization: Unified, challenging benchmarks spanning embodied, linguistic, and programmatic tasks with comprehensive, cross-domain evaluation metrics.

Zero-shot task execution is thus a rapidly evolving field, underpinning progress towards flexible, deployable autonomous agents, with foundational techniques in language-model–driven planning, reward-function encoding, compositional recognition, and cross-modal grounding (Lin et al., 4 Mar 2025, Holomjova et al., 5 Jun 2025, Zhou et al., 2022, Lampinen et al., 2019, Ollivier, 15 Feb 2025, Cui et al., 2022, Mukherjee et al., 2020, Kuo et al., 2020).