Adaptive Visual Planning

• Adaptive visual planning is a decision-making paradigm that tightly integrates perception with planning to address high-dimensional, partially observable environments.
• It leverages learned visual models, active sensing techniques, and modular algorithms for robust performance in robotics, embodied AI, and vision-based control.
• These systems dynamically adapt to observation shifts and changing task objectives, optimizing resource use and ensuring real-time, effective decision making.

Adaptive visual planning refers to a class of decision-making algorithms in which perception, representation, and planning are tightly coupled and dynamically modulated to optimize behavior in complex, partially observed, or changing environments. These methods leverage learned visual models, recurrent or online planning modules, and explicit adaptation mechanisms to handle state uncertainty, varying task objectives, perceptual degradation, and resource constraints. Adaptive visual planning is foundational in robotics, embodied AI, and vision-based control, addressing challenges such as high-dimensional observation spaces, distribution shift, and changing environments.

1. Theoretical Foundations and Problem Formalisms

Adaptive visual planning is broadly grounded in formal sequential decision processes with visual observations, most prominently the partially observable Markov decision process (POMDP). In vision-augmented POMDPs, the system state $s \in S$ is latent, actions $a \in A$ are discrete or continuous motor or sensing commands, and observations $o \in O$ consist of high-dimensional images. Typical formulation is via the tuple $(S, A, O, T, Z, R, \gamma)$, encompassing the transition dynamics $T$, visual observation model $Z(o \mid s)$, and task-specific reward $R$ (Deglurkar et al., 2021).

Perception and planning are integrated through diverse mechanisms:

• Belief representations synthesized from visual observations, such as particle filters with neural likelihoods (Deglurkar et al., 2021, Gupta et al., 2017).
• Active information gathering (fixations, view selection, or path replanning) to reduce state/posterior uncertainty or maximize utility (Wang et al., 18 Sep 2025, Peng et al., 2018, Bai et al., 7 Aug 2025, Xu et al., 15 Dec 2024, Rückin et al., 14 Oct 2024).
• Goal-conditioned visual latent dynamics for long-horizon trajectory inference directly in visual or learned spaces (Pertsch et al., 2020, Xu et al., 16 May 2025).

This tight coupling enables planners to adapt execution in response to new visual information, changes in reward structure, or unforeseen environment conditions.

2. Representative Algorithmic Paradigms

Several families of adaptive visual planners demonstrate this integration:

2.1 Modular POMDP Planners with Learned Visual Models

Visual Tree Search (VTS) (Deglurkar et al., 2021) is a canonical modular system that combines:

• Offline-trained deep generative observation models (e.g., $Z_\theta(o|s)$, a likelihood network; $G_\xi(s)$, a CVAE-based image generator).
• Differentiable particle filter for belief update, with particle proposal adaptation.
• Online Monte Carlo Tree Search (MCTS; PFT-DPW flavor) for planning using generative rollouts conditioned on the current belief. VTS is robust to novel image corruptions and reward modifications without retraining, as adaptation is absorbed at the inference and planning stages.

2.2 Active Visual Token and Fixation Selection

Emerging work frames perception itself as a sequential decision process. AdaptiveNN (Wang et al., 18 Sep 2025) models visual processing as a coarse-to-fine POMDP in which the system learns where to fixate (sample image patches) and when to stop, integrating CNN/ViT-based representations, RL-based policy gradients for fixation, and task-specific objectives (e.g., accuracy vs. compute budget). AdaptVision (Lin et al., 3 Dec 2025) extends this for multi-modal VLMs, enabling selective acquisition of high-resolution visual tokens under a learned controller, with DTPO algorithms managing trade-offs between visual detail and computational cost.

2.3 Visual Planning via RL in Purely Visual Spaces

Methodologies such as VPRL (“Visual Planning via Reinforcement Learning”) (Xu et al., 16 May 2025) eschew language or explicit symbolic reasoning, training large vision-only generative models to predict the next visual state. Adaptive fine-tuning with group-relative policy optimization (GRPO) enables new policies to emerge from generic vision backbones, supporting generalization and transparent roll-outs in visual reasoning tasks.

2.4 Task- and View-Aware Perception for Robotics

Active, task-driven view planning has become essential in robotic manipulation and spatial perception:

• TAVP (Task-Aware View Planning) (Bai et al., 7 Aug 2025) employs an RL-trained exploration policy within a Mixture-of-Experts visual encoder, allowing robotic agents to dynamically select view poses that optimize downstream manipulation performance and feature disentanglement across tasks.
• Multi-robot viewpoint selection frameworks (Xu et al., 15 Dec 2024), for large-scale construction or monitoring, frame the placement of mobile cameras as a multi-objective optimization, adapting viewpoints in real-time as the principal robot’s workflow and environment evolve.

3. Adaptation and Robustness Mechanisms

A core property of adaptive visual planners is their ability to adjust to changes in environment or task specification without full re-training:

• Robustness to Observation Shift: Deep generative observation models, when trained over distributions with varying noise and corruption, enable robust particle filtering and planning even under unseen test-time degradation (Deglurkar et al., 2021).
• Direct Reward Adaptation: Model-based planners (e.g., VTS, MCTS-augmented) can incorporate arbitrary changes in reward structure or objectives online, leveraging explicit reward computation at planning nodes (Deglurkar et al., 2021).
• Latent Representation and Skill Generalization: Multi-agent planning frameworks in the C-LSR paradigm (Lippi et al., 25 Mar 2024) encode agent capabilities and task feasibility in structured latent spaces, and diagnose missing skills by analyzing unassignable symbolic transitions.
• Inference Cost vs. Performance Trade-off: AdaptiveNN demonstrates that computation can be dynamically allocated to the most informative regions, supporting runtime trade-off between accuracy and resource utilization with parameter-free control (Wang et al., 18 Sep 2025, Lin et al., 3 Dec 2025).
• Fast Adaptation in Reasoning-Action Decoupling: Reinforced latent planning architectures (ThinkAct) (Huang et al., 22 Jul 2025) disentangle episodic reasoning from action execution, with high-level plan latents that can be few-shot adapted or updated online as task feedback is received.

4. Applications and Domains

Adaptive visual planning methods have been validated in a broad spectrum of environments:

Domain	Example Task	Methodological Highlights
Mobile robotics	Point-goal/semantic navigation	CMP (Gupta et al., 2017), Select2Plan (Buoso et al., 6 Nov 2024)
Autonomous driving	Multi-agent traffic, hazard avoidance	VLMPlanner (Tang et al., 27 Jul 2025)
Robotic manipulation	Multi-step reasoning, view control	TAVP (Bai et al., 7 Aug 2025), ThinkAct (Huang et al., 22 Jul 2025)
3D Reconstruction	Scene-quality maximization	Adaptive view planning (Peng et al., 2018)
Continual perception	Semantic terrain monitoring	Active path planning (Rückin et al., 14 Oct 2024)
Multi-agent systems	Parallel/heterogeneous execution	C-LSR roadmap (Lippi et al., 25 Mar 2024)
Visual reasoning	Image-based sequential planning	VPRL (Xu et al., 16 May 2025), Vis2Plan (Yang et al., 13 May 2025)

These systems demonstrate advances in label- and compute-efficiency, robustness to occlusions and noise, multi-agent scalability, and real-world hardware deployment.

5. Computational and Label Efficiency

Multiple approaches achieve marked improvements in efficiency through adaptation:

• Token and Fixation Budgeting: AdaptiveNN achieves up to 28× reduction in inference cost on large-scale tasks (e.g., ImageNet, STSD) with no loss in accuracy, via learned fixation stopping criteria (Wang et al., 18 Sep 2025). In vision-language QA, AdaptVision saves up to 2× tokens compared to static baselines at near-maximal accuracy (Lin et al., 3 Dec 2025).
• Active Learning and Path Planning: Informativeness-driven trajectory planning in adaptive path planning frameworks allows 99.5% reduction in necessary human labels while maintaining supervised-level semantic segmentation (Rückin et al., 14 Oct 2024).
• Planning-Time Adaptivity: Symbol-guided planners (Vis2Plan) use symbolic subgoal assembly for >50× reduction in planning latency compared to video-diffusion planners, while attaining >50 percentage points higher success rate in long-horizon manipulation tasks (Yang et al., 13 May 2025).

6. Interpretability, Modular Structure, and Limitations

Adaptive visual planning frequently yields interpretable intermediate products:

• Explicit Belief Maps: CMP and VTS output probabilistic spatial beliefs and trajectories that are visually inspectable (Gupta et al., 2017, Deglurkar et al., 2021).
• Plan Visualization and Subgoal Sequencing: White-box symbolic planners (Vis2Plan (Yang et al., 13 May 2025), C-LSR (Lippi et al., 25 Mar 2024)) return fully decodable plan graphs and image sequences; adaptive fixation patterns in AdaptiveNN often align with human attention (Wang et al., 18 Sep 2025).
• Modular Decomposition: Separation of generative observation, proposal, particle filtering, and planning (VTS (Deglurkar et al., 2021)) or inference-perception-decision blocks provides robustness to distribution shifts.

Limitations include:

• Dependence on Objective-Consistent Rewards: Adaptation is effective only if the reward/uncertainty metrics remain consistent with downstream objectives (Deglurkar et al., 2021, Lin et al., 3 Dec 2025).
• Perceptual Bottlenecks: In difficult domains (e.g., transparent objects in manipulation, severe occlusions), even adaptive perception may fail without additional sensing modalities (Bai et al., 7 Aug 2025).
• Computational Overhead: Some approaches (e.g., autoregressive image generation in VPRL (Xu et al., 16 May 2025)) incur higher runtime costs, though techniques such as token budgeting and symbolic relaxation mitigate this in practice.

7. Open Directions and Perspectives

The landscape of adaptive visual planning continues to expand toward:

• Integration of Active Sensing with Planning: Developing joint architectures that co-optimize sensor control and decision-making under resource constraints (Wang et al., 18 Sep 2025, Lin et al., 3 Dec 2025, Peng et al., 2018).
• Generalizable Representation Learning: Decoupling and modularization (TaskMoE, latent roadmaps) to handle multi-task, cross-domain transfer and extreme data scarcity (Bai et al., 7 Aug 2025, Lippi et al., 2022).
• Real-Time, Continual, and Interactive Learning: Coupling continual model updating with interactive labeling (active path planning, rapid adaptation) for robots in lifelong deployment (Rückin et al., 14 Oct 2024).
• Rich Uncertainty Quantification: Joint modeling of epistemic/aleatoric uncertainty to better target exploration and adapt plans in dynamic or novel conditions (Rückin et al., 14 Oct 2024).

The future development of adaptive visual planning will likely incorporate increasingly active, flexible, and resource-aware perception systems, tightly integrated with planning, underpinned by scalable and interpretable learning mechanisms to achieve robust deployment in challenging, real-world environments.