Feedback-Driven Sub-Task Planning

• Feedback-driven sub-task planning is a method that integrates user, sensor, and system feedback to iteratively modify and optimize action plans.
• Architectures like IteraPlan and Task-Decoupled Planning employ modular decomposition and local re-planning to reduce errors and improve task efficiency.
• Empirical evaluations demonstrate significant enhancements in success rates, efficiency, and adaptability across applications such as robotics and human-robot collaboration.

Feedback-driven sub-task planning is an approach in which the decomposition, execution, and dynamic modification of action sequences are guided and refined by explicit feedback received during or after execution. Unlike open-loop planning, where an agent statically generates a sequence of actions to achieve a goal, feedback-driven systems integrate diverse real-time signals—such as user correction, sensor data, or environmental error messages—to iteratively revise plans at the sub-task or skill level. This methodology is central in human-robot collaboration, embodied agent control, robotics, and complex sequential decision-making for both simulated and real-world environments, enabling greater robustness and adaptability in the face of vagueness, uncertainty, and dynamic execution contexts.

1. Formal Architectures and Mathematical Foundations

Feedback-driven sub-task planning appears in several architectural paradigms, often involving some permutation of modular decomposition, reactive error handling, and closed-loop refinement. The following principles are representative across leading frameworks:

• Plan Representation: Sub-tasks are typically represented as ordered or partially ordered lists (or trees/graphs) of atomic skills, options, or action schemas. Formalizations include sequences $\theta = [s_1, ..., s_N]$, DAGs $G = (V, E)$ of sub-goals, or symbolic structures such as behavior trees.
• Feedback Modeling: Feedback $r$ is modeled as a vector (e.g., $$r_n \in \{\text{success}, \text{failure}\} \times \mathbb{R}^k$$) capturing binary outcomes, natural language corrections, structured error logs, or preference scores.
• Update Protocol: The core dynamical mechanism involves an update function

$\theta_{n+1} = f(\theta_n, r_n)$

often realized as a prompt extension in LLM-based systems, a potential-field modification in myopic descent methods, or a BT/subtree edit in symbolic planners.

• Optimization Criterion: The objective for plan adaptation is to maximize (expected) task success and preference satisfaction:

$$J(\pi_\theta) = \mathbb{E}_{\tau \sim \pi_\theta}[U(\tau)],$$

where $U(\tau)$ quantifies both completion and alignment with user/environmental criteria (Shervedani et al., 2 Mar 2025).

Table: Representative Plan and Feedback Update Formulations

Method	Plan Representation	Feedback Integration	Update Rule/Objective
IteraPlan (Shervedani et al., 2 Mar 2025)	Subtask List $\theta$	Binary/natural language	$\theta_{n+1}=f(\theta_n,r_n)$
Task-Decoupled (Li et al., 12 Jan 2026)	DAG of sub-goals $G$	Scoped error signals	Local replanning in node context
Myopic Descent (Mengers et al., 3 Mar 2025)	Potential fields $V(x)$	Streaming sensory state	Modulate $\alpha_i(t)$, $V_i$
AdaPlanner (Sun et al., 2023)	Code plan $P$	In-plan/out-of-plan signals	Plan tail rewrites; local context
Behavior Trees (Ao et al., 2024)	Hierarchical BT	GUI/natural language edits	$\mathcal{F}(\mathrm{BT}_0, F)$

This diversity of representations and update rules reflects the generality of the feedback-driven paradigm across continuous control, symbolic reasoning, and LLM-based agents.

2. System Architectures and Planning Pipelines

Feedback-driven sub-task planning frameworks exhibit modular architectures that support iterative, response-driven refinement cycles. Prominent examples include:

• IteraPlan (Shervedani et al., 2 Mar 2025): LLM-based task decomposition for human-robot collaboration. The system processes vague user instructions and environment object/location lists via a concise, domain-general prompt to produce an initial sub-task sequence and Python script. Execution in a simulator (AI2-THOR) yields structured error logs; both human and simulator feedback are used to re-prompt the LLM for plan refinement. Affordance-based task allocation heuristics assign sub-tasks to human or robot agents.
• Task-Decoupled Planning (TDP) (Li et al., 12 Jan 2026): Decomposes a long-horizon instruction into a DAG of sub-goals via a Supervisor. Each node is planned and executed in isolation, with feedback affecting only local context. Local replanning is triggered by failures, errors, or contradictory observations, avoiding cross-context error propagation.
• Gradient-Based Reactive Sequencing (Mengers et al., 3 Mar 2025): Sub-task transitions and execution emerge from online modulation of a composite potential field. Feedback from the world and recursive state estimators dynamically alters the weights and shapes of component potentials, causing the system to sequence and adapt sub-actions without explicit symbolic plans.

The essential structure is a feedback loop where the plan or subplan is continually (re-)generated and/or mutated in light of structured feedback from the environment or user, yielding robust adaptation.

3. Types of Feedback and Integration Mechanisms

Feedback modalities and their operational roles vary depending on system scope, but include:

• Binary signals: Success/failure flags for each skill or plan segment—can be generated by a human (code approval), a simulator (execution trace), or environment sensors.
• Structured error messages: Execution errors such as “Object not reachable,” “Collision at step 3: Pan not on Stove,” or controller returns (pass/fail) can be directly fed back to the planner to elicit plan modification (Shervedani et al., 2 Mar 2025, Bhat et al., 2024).
• Natural language/Human preferences: Arbitrary corrections or user directives (e.g., “Use olive oil instead of butter”) are interpreted as preference injection, causing context-specific plan mutation.
• Sensor and context feedback: Sensor arrays or force-torque data streams are used to trigger control primitives or subtask transitions (e.g., in DoorBot (Wang et al., 12 Apr 2025)).

Integration strategies include prompt extension for LLMs, tree/subtree edits in symbolic planners, and continuous controller adaptation in closed-loop physical systems. Mathematical treatment varies: explicit update functions in plan parameter space, node-local context resets in DAGs, or policy-reweighting in neural planners.

4. Symbolic and Hierarchical Decomposition Strategies

Feedback-driven sub-task planning leverages a variety of symbolic and hierarchical representations:

• Sequential and hierarchical plans: Ordered lists, behavior trees, and hierarchical task networks provide structured decomposition. LLM-based systems generate action sequences or tree-structured policies that modularize plan refinement, allowing subtrees or plan tails to be rewritten as feedback arrives (Ao et al., 2024).
• DAG-based task decoupling: Explicit decomposition into dependency graphs allows context-scoped replanning and prevents upstream errors from affecting unrelated future sub-tasks (Li et al., 12 Jan 2026).
• Options and reward-respecting subtasks: In reinforcement learning frameworks, temporally abstract options defined by reward-respecting subgoals enable planning at multiple abstraction levels, with feedback guiding option discovery and value estimation (Sutton et al., 2022).
• Potential fields and implicit sequencing: Non-symbolic controllers can implicitly encode sub-tasks as local minima or steepest-gradient directions in composite potential fields, automatically adjusting sequencing according to feedback-modulated context variables (Mengers et al., 3 Mar 2025).

A key observation is that robust feedback-driven decomposition can be realized in both discrete symbolic and continuous control domains.

5. Empirical Evaluation and Performance Metrics

Empirical benchmarks across multiple domains confirm the efficacy of feedback-driven sub-task planning. Representative metrics and findings include:

• Optimal Code Rate (OCR) and Successful Execution Rate (SER): In IteraPlan (Shervedani et al., 2 Mar 2025), the fraction of runs requiring $\leq k$ refinements to reach zero-error code (OCR) and fraction of error-free executions (SER) increase sharply with feedback-driven refinement. For instance, a single human or LLM feedback loop ($k=1$) increased OCR from 0.35 (zero-shot) to 0.85 in kitchens, and two loops raised SER from 0.60–0.85 to $\approx 1.00$.
• Efficiency and robustness improvements: TDP (Li et al., 12 Jan 2026) demonstrated up to 82% reduction in token consumption versus entangled planners, while maintaining or exceeding baseline performance on TravelPlanner, ScienceWorld, and HotpotQA.
• Real-world reliability: DoorBot (Wang et al., 12 Apr 2025) achieved a 90% success rate for opening diverse unseen doors with haptic feedback, compared to 50–55% for vision-only or open-loop planners.
• Sample efficiency: AdaPlanner (Sun et al., 2023) outperformed training-intensive baselines (e.g., CC-Net, Reflexion) in ALFWorld and MiniWoB++ with 2x to 600x fewer demonstrations, driven by code-style plan refinement and feedback.
• Implicit learning curves: In reward-respecting subtask RL (Sutton et al., 2022), planning backups and sample complexity were reduced by up to 50% compared to eigenoptions or shortest-path sub-goals.

Further, ablation studies confirm that removing feedback loops or restricting feedback scope significantly degrades success rates and increases error propagation.

6. Limitations and Ongoing Challenges

Despite substantial advances, several limitations constrain current feedback-driven sub-task planning approaches:

• Human-in-the-loop dependency: Systems such as IteraPlan and LLM-based BT generators require structured human feedback for optimal performance; this can limit scalability and impose burdens on non-technical users (Shervedani et al., 2 Mar 2025, Ao et al., 2024).
• Affordance and perception challenges: Affordance-based allocation by LLMs remains suboptimal compared to heuristic rules (CASR of 0.76 vs. 1.00 in IteraPlan), indicating a need for richer multimodal perception (Shervedani et al., 2 Mar 2025).
• Generalization and convergence: No formal guarantee that feedback-driven iterative refinement always converges to a globally valid plan; observed stability is empirical (Ao et al., 2024).
• Real-world uncertainty: Sim-to-real transfer, noisy perception, and complex failure cases (irreversible steps, highly stochastic domains) pose open research problems (Shervedani et al., 2 Mar 2025, Wang et al., 12 Apr 2025).
• Sample and computational efficiency: Token/cost-intensive LLM usage and memory requirements for policy or plan storage remain an issue for large-scale deployment (Ao et al., 2024, Sun et al., 2023).
• Hierarchical bias: Some purely gradient-based controllers can fail on tasks requiring non-myopic, globally optimal action selection (e.g., Towers of Hanoi) (Mengers et al., 3 Mar 2025).

Research is ongoing in integrating explicit policy optimization, learning adaptive granularity in task decomposition, and scaling skill discovery to minimize human supervision.

7. Applications and Future Directions

Feedback-driven sub-task planning is established as a central methodology in long-horizon robotics, embodied AI, and interactive systems. Ongoing and prospective directions include:

• Human-robot interaction: Vague-to-plan conversion and online adaptation in household or collaborative robots (Shervedani et al., 2 Mar 2025).
• Autonomous open-world agents: Robust skill chaining and error-driven plan modification in domains such as Minecraft and task-oriented web interaction (Wang et al., 2023, Sun et al., 2023).
• Hierarchical RL: Model-based planning using reward-respecting options; adaptation to feature drift and transfer learning (Sutton et al., 2022).
• Physical manipulation: Closed-loop haptic feedback for robust object and articulated mechanism manipulation in unstructured settings (Wang et al., 12 Apr 2025).
• Behavioral cloning with feedback: Efficient sample use via plan refinement and skill discovery libraries (Sun et al., 2023).
• Symbolic planning with LLMs: Zero-shot, modular task-planning reusable across domains, with explicit correction pipelines (Ao et al., 2024).

A plausible implication is that as perception and representation learning become more robust and feedback models more scalable, the domain of applicability will further expand toward fully autonomous, context-adaptive agents operating safely and efficiently in dynamic, uncertain, and ambiguous environments.