Title: Seeing through Uncertainty: Robust Task-Oriented Optimization in Visual Navigation
Venue: NeurIPS 2025
Accepted: yes
Keywords: Visual Navigation

Deep Reinforcement Learning

Task-based Model

Uncertainty Calibration
Abstract: Visual navigation is a fundamental problem in embodied AI, yet practical deployments demand long-horizon planning capabilities to address multi-objective tasks. A major bottleneck is data scarcity: policies learned from limited data often overfit and fail to generalize OOD. Existing neural network-based agents typically increase architectural complexity that paradoxically become counterproductive in the small-sample regime. This paper introduce NeuRO, a integrated learning-to-optimize framework that tightly couples perception networks with downstream task-level robust optimization. Specifically, NeuRO addresses core difficulties in this integration: (i) it transforms noisy visual predictions under data scarcity into convex uncertainty sets using Partially Input Convex Neural Networks (PICNNs) with conformal calibration, which directly parameterize the optimization constraints; and (ii) it reformulates planning under partial observability as a robust optimization problem, enabling uncertainty-aware policies that transfer across environments. Extensive experiments on both unordered and sequential multi-object navigation tasks demonstrate that NeuRO establishes SoTA performance, particularly in generalization to unseen environments. Our work thus presents a significant advancement for developing robust, generalizable autonomous agents.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes NEURO, a hybrid framework that integrates deep neural perception with downstream robust optimization for visual navigation tasks in partially observable, data-scarce environments. The method leverages Partially Input Convex Neural Networks (PICNNs) to construct convex uncertainty sets, which are then used in a robust optimization formulation to guide planning and action selection. NEURO enables bidirectional interaction between learning and planning modules by incorporating optimization-derived feedback into policy learning. Experiments on MultiON tasks (both unordered and sequential) demonstrate improved performance and generalization over purely neural or heuristic baselines, with additional ablation studies validating key design choices.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n1. Innovative hybrid formulation: The integration of neural predictions with a robust optimization planner is novel and well-motivated, offering a principled way to address uncertainty in partially observable navigation tasks.\n2. Strong empirical results: NEURO consistently outperforms baselines on MultiON benchmarks under different target counts, especially in generalization to unseen environments.\n3. Comprehensive ablations: The paper includes detailed ablation studies on the action blending factor, spatial-temporal optimization scale, and uncertainty coverage level, demonstrating careful empirical analysis.\n4. Solid mathematical derivation: The core optimization formulation, uncertainty calibration, and dual problem transformation are all mathematically well-grounded and derived in detail.\n\nWeaknesses\n1. The paper lacks runtime and computational cost analysis, raising concerns about the feasibility of real-world robotic execution, especially given the optimization overhead.\n2. The paper does not include any analysis or discussion of failure cases.\n3. The comparison includes only one method from 2024; incorporating more recent baselines would strengthen the empirical validation.\n4. The optimization module depends on accurate belief estimation via predicted object transition matrices, which are not directly supervised. The paper does not analyze how sensitive the method is to inaccurate belief estimation, which is critical for robust planning in partially observable environments.\n5. The convexity assumption imposed by PICNNs may be overly idealized, as real-world visual uncertainty is often non-convex or multimodal. While the authors acknowledge this limitation in the conclusion, its practical impact (e.g., performance degradation in non-convex scenarios) is not empirically analyzed.\n6. The theoretical coverage guarantee for the uncertainty set (Proposition 1) relies on the i.i.d. assumption for calibration data, which may not hold in embodied navigation settings with strong temporal correlations between observations.\n\nQUESTIONS:\nAs outlined in the Weaknesses, my main concerns center around missing runtime/failure analysis and the sensitivity to belief estimation. Addressing these with additional experiments or clarifications would help resolve key doubts and could improve my overall assessment.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-21"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes NEURO, a hybrid learning + optimization framework for Multi-Object Navigation (MultiON). A visual-perception module first encodes RGB-D observations, egocentric maps and goal embeddings into features. These features are fed to Partially Input Convex Neural Networks (PICNNs), which output calibrated convex uncertainty sets over object-transition probabilities via a conformal-prediction procedure. \nA downstream robust pursuit–evasion optimization problem then plans an action trajectory that maximizes discounted capture belief while respecting motion constraints. The optimization result is blended with the network’s own policy and its objective value is used as an auxiliary reward, enabling end-to-end training by differentiating through the Karush-Kuhn-Tucker conditions.\n\nExperiments on both sequential (S-MON) and unordered (U-MON) MultiON benchmarks show that NEURO outperforms prior methods on unseen environments and converges faster during training. \nAblations illustrate the effect of action-blending weight, grid/time-horizon scale, and conformal coverage.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths :\n\na. Conceptual novelty – first task-based, uncertainty-aware optimization layer tightly integrated with visual navigation; extends differentiable‐optimization literature to long-horizon POMDPs.\n\nb. Principled uncertainty handling – PICNN + conformal calibration guarantees (marginal) coverage of true dynamics, keeping the downstream RO convex and tractable.\n\nc. Empirical gains & efficiency – consistent SOTA on S-MON/U-MON and faster convergence with modest inference overhead\n\nd. Careful ablations – studies on \\lambda, grid size, coverage level, and task-specific vs. generic formulations clarify where gains come from.\n\nWeaknesses:\n\na. Convex approximation limits expressiveness; many real object-movement distributions are non-convex → possible sub-optimal plans. Authors should aim at quantifying this.\n\nb. Evaluation confined to simulation; no physical-robot or real-world visual distractors, lighting changes, sensor noise, adversarial objects. Which is very important in these robot papers.\n\nc. Scalability of RO – solving large grids (E ≥ 5, τ ≥ 6) already multiplies inference time; How does the method extend to very large houses or outdoor scenes uncertain.\n\nd. **Important:** The writing is unclear; the authors assume considerable prior knowledge, which makes the paper difficult for non-expert readers to follow.\n\nOverall, the strengths outweigh the weaknesses. While I would like to see the shortcomings addressed, I remain inclined to recommend acceptance of the work.\n\nQUESTIONS:\na. Real-robot feasibility – have you profiled NEURO to demonstrate closed-loop runs on an actual mobile robot?\n\nb. Sensitivity to PICNN design – how do calibration quality and task performance vary with PICNN capacity or regularization?\n\nLIMITATIONS:\nSee above\n\n--------------\nReview Update: Concerns Addressed - score increased to 5", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 2, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis work proposes NEURO, a hybrid framework for visual navigation that converts network predictions into calibrated convex uncertainty sets via Partially Input Convex Neural Networks (PICNNs) and then employs robust optimization to plan actions under partial observability, achieving SOTA performance and generalization on multi-object navigation benchmarks.\n\nSTRENGTHS AND WEAKNESSES:\n### Strengths: \n- The presentation is clear and easy to follow.\n- Recasting multi-target object navigation as a pursuit-evasion problem is novel because previous research has relied almost exclusively on POMDP formulations; this alternative perspective can open new avenues for multi-target object navigation.\n- Calibrated uncertainty sets directly inform the planner, a step toward reliable agents that must quantify risk.\n- The method (NEURO) significantly outperforms baselines in the testing (unseen) scenarios, suggesting better generalization performance.\n- Provides the full codebase to reproduce the results.\n### Weaknesses:\n- Many results (e.g., in Table 1, Table 3, and Figure 3) lack measures of statistical significance such as variance or confidence intervals.\n- The experiments omit recent baselines, e.g., PoliFormer [1]\n- While this work focuses on multi-object navigation, including results for the single-object setting ($m=1$), would be helpful and strengthen the experiments section.\n\n[1] PoliFormer: Scaling On-Policy RL with Transformers Results in Masterful Navigators, Zeng et al., CORL 2024.\n\nQUESTIONS:\n- Could the authors please report results averaged over multiple random seeds? Many results lack measures of statistical significance, such as variance or confidence intervals.\n- Why are some results of HTP-GCN in Table 1 missing?\n\nI'm willing to increase the score if the authors address the outlined questions and limitations.\n\nLIMITATIONS:\nSee weaknesses above.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors present a hybrid framework (NeuRO) that combines conventional neural network perception and policy modules with a task-based robust optimization pipeline to address data scarce scenarios which will often lead to poor generalization performance (out-of-distribution failure) for typical learned neural network approaches when faced with unseen scenarios during testing.\n\nThey apply their approach to the visual navigation task of sequential or unordered Multi-Target Object Navigation (MultiON) where the agent must navigate between a series of goals (represented by the $\\mathbf{G_t}$ vector). The agent operates on RGB-D image inputs, maintains an egocentric map and operates under grid-based actions (described in lines 91-100). \n\nTheir approach is composed of a number of modules and an overview of the proposed NeuRO method is illustrated in Figure 2. Namely a base neural network perception module, a Partially Input Convex Neural Network (PICNN) to convert the prior potentially unreliable neural network outputs to a set of calibrated convex uncertainty sets, and a robust optimization pipeline.\n\nThe base neural network perception module is described in section 3.1 (page 4) and forms the baseline method without the proposed robustness modifications. This perception network ultimately learns latent state $s_t$ and features $f_t$ from which an action policy network $\\pi(a_t^{\\mathrm{net}}|f_t)$ is formed.\n\nIn section 3.2 (page 4), their formulism for a robust optimization planner is presented. They describe a notion of uncertainty through a set of object transition matrices $M^i$ (representing the i-th goal object's probability to move between cells each each $M^i_{uv}\\in M^i$) on a discretized grid along with additional terms for prior $(\\beta_i^t)_v$ and posterior $(\\alpha_i^t)_v$ beliefs representing the agent's confidence that a target object occupies the cell. Using these terms, they seek to optimize the objective function of finding an agent path that maximizes the accumulated discounted capture beliefs (equation 4).\n\nSection 3.3 (page 5), describes the usage of PICNNs to output a calibrated convex set of the prior discussed transition matrices and finally section 3.4 (page 6) describes how the resulting action $a_t^{\\mathrm{task}}$ and reward $r_t^{\\mathrm{task}}$ from the robust optimization procedure are used modulate the policy network and agent reward signal.\n\n__Novelty and Contributions:__\n\nThe main novelty of the approach is to provide feedback from a robust optimization pipeline to a conventional neural network action policy. They do this by first using a Partially Input Convex Neural Network (PICNN) to determine a calibrated convex uncertainty set of object-tracking transition matrices from the base neural network's state vector. A robust optimization problem is solved using this set, and the resulting $a_t^\\mathrm{task}$ and $r_t^\\mathrm{task}$ are used to modulate the original action policies perceived reward and executed action (this overall process is described in section 3.4). They assert that this uncertainty-aware approach for tracking target objects during visual navigation is well suited for generalizing the the test environment under data scarce scenarios.\n\n__Experiments:__\n\nDuring experiments, the authors compare against and outperform a number of state of the art baselines (Table 1) in sequential and unordered multi-target object navigation (section 4.1). They hypothesize that the gain in formation is due to 1) the agent leveraging the additional reward and action signal from the downstream robust optimization solution and 2) the method’s ability to coordinate between multiple targets.\n\nIn section 4.2, the authors compare the NeuRO method vs the augmented neural network policy where their method obtains a significantly higher success rate versus training data (Figure 3).\n\nFinally, experimental section 4.3 evaluates various ablation studies.\n\nSTRENGTHS AND WEAKNESSES:\n__Strengths:__\n\n- __Novelty and performance (originality and significance):__ Novel approach which makes use of reward and action feedback from a downstream robust optimization process to modulate the agent's perceived rewards and action policy. This is an especially useful method for the case of data scarcity (such as for robotics). They note improved performance versus a wide array of existing state of the art baselines (Table 1). This process is further described above in the summary section.\n- __Breadth of experiments (quality):__ This is done well for the most part (however, see weaknesses section below for additional comments). Comparison against range a range baselines is done (Table 1). Intuition regarding the improved results is provided (the agent leveraging the additional reward and action signal from the downstream robust optimization solution and 2) the method’s ability to coordinate between multiple targets). Ablation studies and hyper-parameter sweeps are also presented to provide justification regarding the selected method configuration. The authors further provide intuition as to the effects of these hyper parameters.\n- __Manuscript clarity:__ The manuscript is generally well written and generally easy to understand.\n\n__Weaknesses:__\n\n- __Experimental rigour (quality):__ Results in experiments lack any standard deviation / confidence interval metrics for repeated trials (for example with different network random seeds). This would be especially important for some metrics which are quite close to baseline performance in Table 1 (PPL for S-mon (m=2) and Progress for S-Mon (m=3)).\n- __Potential limited use (significance):__ The method appears to be limited to a discrete grid for object locations. In addition, the detection factor ($d(p_t)_v$ in equation 3b) seems to be a relatively simple distance scale. I would of expected this value to account for more information in the scene such as line of sight and obstacle occlusion as well. Likewise, the inference times in table 3 seem relatively large and require further clarification if this method can be used in real-time. See questions to below to authors for clarification.\n- __Experimental description (clarity):__ There is a very limited amount of context when beginning to describe the experimental setup in section 4. Ideally I feel a brief introduction should be given and a reference to relevant sections in the appendix if needed.\n\n__Overall Assessment and Suggestions for Improvement:__\n\nCurrently I have marked this work as borderline accept as it is novel and beats existing state of the art baselines (see strengths section above). However I feel that it could be improved with additional confidence interval analysis in the experiment section (see weaknesses above). There are also several limitations (noted in the weaknesses section) and the authors' own discussion regarding limitation to convex uncertainty sets (discussed on lines 329-335).\n\n__Minor Points:__\n\n- Lines 114-115 and 116-117 appear to repeat the same information. \n- On line 163, I believe it should read “$i$ for conciseness”.\n- Table 3 appears in the text before table 2. I imagine the label for these should be switched?\n\nQUESTIONS:\n- The detection factor ($d(p_t)_v$ in equation 3b) seems to be a relatively simple distance scale. I would of expected this value to be based on line of sight / obstacle occlusion as well. Can this be clarified? Is line of sight information incorporated in some other fashion when updating the target object belief state?\n\n- In Table 3, could the authors please clarify what precisely \"Inference time\" refers to? On line 282 it states \"inference time for solving the optimization problem...\" Is this inference time only relevant during the training process to provide a reward feedback for training the agent's policy (as in Algorithm 1 on page 6) or is it also required during the testing phase? Currently this amount seems quite high for real-world usage if it is required at runtime.\n\n- Given the method appears to be currently tied to a discrete grid for the transition matrices for representing object locations, do the authors have any thoughts on making use of this method in a continuous space and what the implications of that modification might be?\n\nLIMITATIONS:\nYes. Limitations are listed by the authors on lines 329-335.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}