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arxiv: 2606.04355 · v1 · pith:XW2FJKVO · submitted 2026-06-03 · cs.RO

Think Fast and Far: Long-Horizon Online POMDP Planning via Rapid State Sampling

Yuanchu Liang , Edward Kim , J. Arden Knoll , Wil Thomason , Zachary Kingston , Lydia E. Kavraki , Hanna Kurniawati This is my paper

Reviewed by Pith T0 review T1 audit T2 compute T3 formal T4 kernel 2026-06-28 06:40 UTCgrok-4.3pith:XW2FJKVOrecord.jsonopen to challenge →

classification cs.RO
keywords POMDPonline planningsampling-based motion planningbelief spacemacro actionslong-horizon planningrobotics under uncertainty
0
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The pith

ROP-RAS3 solves long-horizon POMDPs by rapidly sampling macro actions online to bias belief-space sampling without exhaustive action enumeration.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces an approximate online POMDP solver named ROP-RAS3 that applies fast sampling-based motion planning to generate a diverse set of macro actions directly in the state space. These macro actions then guide belief-space sampling so that high-quality policies can be inferred for problems whose action spaces would otherwise be too large to enumerate fully. Convergence occurs at a rate governed by the number of sampled actions rather than the total size of the action space, allowing the approach to address instances with thousands of lookahead steps and state spaces up to 35 dimensions. The method is shown to handle continuous, discrete, and hybrid spaces and produces higher success rates than prior solvers on the tested long-horizon tasks.

Core claim

ROP-RAS3 uses novel extremely fast sampling-based motion planning techniques to sample the state space and generate a diverse set of macro actions online, which are then used to bias belief-space sampling and infer high-quality policies without requiring exhaustive enumeration of the action space. ROP-RAS3 converges to a near-optimal reference-based solution at a rate that depends on the number of sampled actions, rather than the size of the action space. Although the reference-based optimal solution may not be the same as the optimal POMDP solution, empirical results indicate that in all of these problems, in terms of success rate, ROP-RAS3 outperforms other state-of-the-art methods by up t

What carries the argument

Rapid state-space sampling that produces online macro actions to bias belief-space sampling and policy inference in long-horizon POMDPs.

If this is right

  • Policy quality improves as more macro actions are sampled, independent of the underlying action-space cardinality.
  • The solver remains applicable when state, action, and observation spaces are continuous, discrete, or mixed.
  • Problems with up to 3000 lookahead steps and 35-dimensional states become tractable.
  • Physical robot execution is feasible once the macro-action set is generated online.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If macro-action diversity is the main performance driver, then substituting a different sampling-based planner could further improve results without changing the POMDP solver.
  • The separation between reference solution and true optimum may become visible on tasks where exhaustive search is still feasible, offering a direct way to quantify approximation error.
  • The same sampling idea might transfer to other belief-space planners that currently rely on fixed or hand-designed action sets.

Load-bearing premise

The reference-based optimal solution obtained via sampled macro actions is a sufficiently close proxy to the true POMDP optimum for the target tasks.

What would settle it

A long-horizon POMDP instance where the macro actions sampled by the motion planner produce reference-based policies whose success rate falls below that of an exhaustive-enumeration baseline or other state-of-the-art solvers.

Figures

Figures reproduced from arXiv: 2606.04355 by Edward Kim, Hanna Kurniawati, J. Arden Knoll, Lydia E. Kavraki, Wil Thomason, Yuanchu Liang, Zachary Kingston.

Figure 1
Figure 1. Figure 1: Navigation benchmark environments. All modes of starting locations, if any, are marked in orange. Goals are marked in green. Purple boxes, if any, indicate observation zones. Sampled belief particles are displayed in Yellow and the opacity denotes the weight of the particle. Red boxes are danger zones. Fixed walls are marked in gray and randomly sampled obstacles are marked in cyan. (a) Sphere-Search (b) R… view at source ↗
Figure 2
Figure 2. Figure 2: Manipulation benchmark environments. Belief particles of observed obstacles are displayed in yellow. In Sphere-Search, light is denoted as a purple sphere. Goals are marked in green. Obstacles are marked in gray. In Ray-Detect, obstacles are placed on a table. Rays from the arm is displayed as a red straight line. In Shelf-Move, movable obstacles are the cylinders inside the shelf. Ray-Detect (Figure 2b). … view at source ↗
Figure 3
Figure 3. Figure 3: Stretch demonstrations using ROP-RAS3. It smartly navigates around the moving pedestrian and quickly reaches the goal without waiting for the pedestrian or colliding with the environment. the agent for reaching the goal. A -800 penalty is provided for colliding with the pedestrian. A -20 penalty is given for being too close (within 0.3m) to the pedestrian. Otherwise, a -1 penalty is given for each primitiv… view at source ↗
Figure 4
Figure 4. Figure 4: Ablation Study of ROP-RAS3’s performances with ϵ-Exploration in Maze2D and Multi-Drone. 5.6.2 Effect of Tree Search Depth. Hyperparameters such as the tree search depth are important to the performance of ROP-RAS3. Under tight computational budgets (e.g., one second of planning), the right tree search depth needs to balance collecting long horizon information and Monte Carlo estimation accuracies. This abl… view at source ↗
Figure 5
Figure 5. Figure 5: We see that performance drops as the tree depth increases or decreases; a good rule-of-thumb we found is to set the tree depth to be roughly the number of steps needed to solve the problem under deterministic settings [PITH_FULL_IMAGE:figures/full_fig_p014_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Light Dark is the easiest problem and the agent can quickly navigate to the goal [PITH_FULL_IMAGE:figures/full_fig_p021_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: In Maze, the agent avoids the corridors with danger zones and starts going towards right first to localize. As it navigates to the middle corridor, it needs to carefully pick landmarks to receive observations of itself. Sometimes, the agent uses walls to align all the beliefs on one side as a localization mechanism, and eventually managed to reach the goal. Prepared using sagej.cls [PITH_FULL_IMAGE:figure… view at source ↗
Figure 8
Figure 8. Figure 8: In Random3D, the agent firstly went to the closest landmark on the way to the goal. It then sticks to the wall as it’s a robust path, belief particles can all aligned to the wall, avoiding the danger zones and efficiently navigate to the goal [PITH_FULL_IMAGE:figures/full_fig_p022_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: In Multi-Drone, ROP-RAS3 commands the drones to spread out to detect the target. Then one drone aims to chase a downward moving target to force it to teleport to the other side where another agent has been waiting to capture the target. Prepared using sagej.cls [PITH_FULL_IMAGE:figures/full_fig_p022_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Behaviors of ROP-RAS3 across all manipulation tasks. In Sphere Search (1st row), the manipulator firstly reaches the light (purple) to receive an observation on where the sphere (green) is spawned. Then it navigates directly towards the sphere, avoiding the obstacles (gray). In Ray Detect (2nd row), the manipulator spends extra steps to detect two important obstacles colored in blue and brown. As the unce… view at source ↗
Figure 11
Figure 11. Figure 11: Stretch demonstrations for B-VAMP (first two pictures) and R-POMCP (last two pictures). B-VAMP moves forward and collide with the moving pedestrian due to lack of POMDP planning and R-POMCP tries to avoid the person but went too far and collided with the environment. Prepared using sagej.cls [PITH_FULL_IMAGE:figures/full_fig_p023_11.png] view at source ↗
read the original abstract

Partially Observable Markov Decision Processes (POMDPs) are a general and principled framework for motion planning under uncertainty. Despite tremendous improvement in the scalability of POMDP solvers, long-horizon POMDPs remain difficult to solve. To alleviate the difficulty, this paper proposes a new approximate online POMDP solver, called Reference-Based Online POMDP Planning via Rapid State Space Sampling (ROP-RAS3). ROP-RAS3 uses novel extremely fast sampling-based motion planning techniques to sample the state space and generate a diverse set of macro actions online, which are then used to bias belief-space sampling and infer high-quality policies without requiring exhaustive enumeration of the action space -- a fundamental constraint for modern online POMDP solvers. ROP-RAS3 converges to a near-optimal reference-based solution at a rate that depends on the number of sampled actions, rather than the size of the action space. ROP-RAS3 is evaluated on various long-horizon POMDPs with up to 3000 lookahead steps and 35-dimensional state spaces, where the state, action and observation spaces can be continuous, discrete, or a hybrid of discrete and continuous. Although the reference-based optimal solution may not be the same as the optimal POMDP solution, empirical results indicate that in all of these problems, in terms of success rate, ROP-RAS3 outperforms other state-of-the-art methods by up to multiple folds. We also demonstrate the capability of our approach on a physical robot demonstration. This work extends the theory and empirical results of our ISRR24 paper. Code can be found at \texttt{https://github.com/RDLLab/ROPRAS3}.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript proposes ROP-RAS3, an approximate online POMDP solver for long-horizon problems that uses extremely fast sampling-based motion planning to generate diverse macro actions online. These macro actions bias belief-space sampling to infer policies without exhaustive action enumeration. The central claims are that ROP-RAS3 converges to a near-optimal reference-based solution at a rate depending on the number of sampled actions (not |A|), and that it empirically outperforms state-of-the-art methods by up to multiple folds in success rate across tested domains with up to 3000 lookahead steps and 35D states (continuous, discrete, or hybrid). The work extends an ISRR24 paper, includes a physical robot demo, and provides open code.

Significance. If the convergence claim holds with supporting analysis, the decoupling from action-space size via macro-action sampling would be a meaningful advance for scalable long-horizon POMDP planning under uncertainty. The open code and robot demonstration are concrete strengths supporting reproducibility. The empirical gains, while post-hoc, span diverse domains and could indicate practical utility if baselines and reference optimality are clearly defined.

major comments (2)
  1. [Abstract] Abstract: The claim that 'ROP-RAS3 converges to a near-optimal reference-based solution at a rate that depends on the number of sampled actions, rather than the size of the action space' is presented without a derivation, error bounds, or formal definition of the reference-based optimality measure or how it is computed/measured in this manuscript.
  2. [Abstract] Abstract and evaluation sections: The empirical claim of outperformance 'by up to multiple folds' in success rate relies on post-hoc comparisons whose baseline implementations, reference-solution computation, and statistical controls are not detailed, making it difficult to assess whether the gains are load-bearing evidence for the method's advantage.
minor comments (1)
  1. [Abstract] The manuscript states it extends the theory of the ISRR24 paper but does not reproduce or cite specific equations from that work that establish the convergence rate, which reduces self-containment for readers.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed review and for highlighting the potential significance of the macro-action sampling approach. We address each major comment below with specific plans for revision where the manuscript can be strengthened without misrepresentation.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The claim that 'ROP-RAS3 converges to a near-optimal reference-based solution at a rate that depends on the number of sampled actions, rather than the size of the action space' is presented without a derivation, error bounds, or formal definition of the reference-based optimality measure or how it is computed/measured in this manuscript.

    Authors: The manuscript defines reference-based optimality in Section 3.1 as the optimal policy under the sampled macro-action set (as opposed to the full action space) and provides a convergence argument in Section 3.2 showing that the value gap scales with the number of samples rather than |A|. This analysis is extended from the ISRR'24 precursor. The abstract condenses the result. To improve accessibility we will revise the abstract to briefly define the reference measure and add an explicit forward reference to Section 3.2. We note that the current analysis does not supply explicit finite-sample error bounds under general POMDP assumptions; adding such bounds would require additional technical development beyond the present scope. revision: partial

  2. Referee: [Abstract] Abstract and evaluation sections: The empirical claim of outperformance 'by up to multiple folds' in success rate relies on post-hoc comparisons whose baseline implementations, reference-solution computation, and statistical controls are not detailed, making it difficult to assess whether the gains are load-bearing evidence for the method's advantage.

    Authors: We agree that the evaluation section would benefit from greater transparency. In the revised manuscript we will expand the experimental setup subsection to document: (i) exact hyper-parameters and code versions used for each baseline, (ii) the procedure for obtaining reference solutions (long-horizon Monte-Carlo rollouts with the full macro-action set plus a high-fidelity simulator), and (iii) statistical controls including the number of independent trials, confidence intervals, and any hypothesis tests performed. These additions will be placed in both the main text and the supplementary material. revision: yes

Circularity Check

0 steps flagged

Minor self-citation to ISRR24; central claims on convergence and empirical gains remain independent

full rationale

The paper introduces ROP-RAS3, a new online POMDP solver using rapid state sampling for macro actions, and states convergence to a near-optimal reference-based solution at a rate depending on the number of sampled actions (not |A|). It explicitly caveats that the reference solution may differ from the true POMDP optimum. The sole self-reference is the closing sentence noting extension of ISRR24 theory/empirics; this is not load-bearing for the new convergence rate claim or the reported multi-fold success-rate gains, which are presented as observed across tested domains. No equations, fitted parameters, or derivation steps are shown that reduce the stated results to self-definitions or prior-work fits by construction. The derivation chain is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The approach rests on standard POMDP modeling assumptions and the domain assumption that sampling-based motion planners can rapidly produce a diverse, useful set of macro actions that substitute for full action-space enumeration. No free parameters or invented entities are described in the abstract.

axioms (2)
  • domain assumption POMDPs provide a general and principled framework for motion planning under uncertainty
    Opening sentence of the abstract.
  • domain assumption Sampling-based motion planning techniques can be used to sample the state space and generate macro actions sufficiently fast and diverse for online use
    Core of the proposed method description.

pith-pipeline@v0.9.1-grok · 5855 in / 1462 out tokens · 45952 ms · 2026-06-28T06:40:44.256855+00:00 · methodology

discussion (0)

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