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arxiv: 2604.14965 · v1 · submitted 2026-04-16 · 💻 cs.RO

POMDP-based Object Search with Growing State Space and Hybrid Action Domain

Yongbo Chen , Hesheng Wang , Shoudong Huang , Hanna Kurniawati This is my paper

Pith reviewed 2026-05-10 10:40 UTC · model grok-4.3

classification 💻 cs.RO
keywords POMDPobject searchroboticsMonte Carlo Tree Searchgrowing state spacehybrid action spaceindoor environmentsbelief tree reuse
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The pith

A POMDP solver using MCTS with neural filtering and k-center clustering handles growing states and hybrid actions to localize objects faster than baselines in indoor 3D environments.

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

The paper frames object search as a high-dimensional POMDP whose state space grows as the robot explores and whose actions mix continuous and discrete choices in three dimensions. It introduces the GNPF-kCT solver that reuses belief trees across growing states, filters primitive actions with a neural process network, discretizes high-dimensional spaces via k-center clustering hyperspheres, and selects expansions with a modified UCB that accounts for belief differences. A guessed-target grid-world model supplies extra reward signals when observations are sparse. These pieces together let the robot select actions that reduce uncertainty about target location more efficiently than prior POMDP and non-POMDP methods under identical perception constraints, as verified in Gazebo trials with Fetch and Stretch robots plus real office tests.

Core claim

By modeling object search as a POMDP with explicitly growing state space and hybrid action domain, and solving it online via GNPF-kCT (MCTS with belief-tree reuse, neural-process action filtering, k-center clustering discretization, and belief-difference UCB), the approach yields faster and more reliable target localization than POMDP baselines and SOTA non-POMDP solvers, including LLM-based methods, under fixed computational budgets and perception pipelines; the guessed-target grid-world strategy further improves performance in low-information regimes, with convergence supported by theoretical analysis.

What carries the argument

The growing neural process filtered k-center clustering tree (GNPF-kCT), which performs online MCTS on a growing-state POMDP by reusing belief trees, filtering useless actions with a neural process network, discretizing hybrid action spaces with k-center hyperspheres, and guiding expansion with a modified UCB based on belief differences and cell diameters.

If this is right

  • Object-search policies remain tractable even as the robot's map and belief grow during exploration.
  • High-dimensional hybrid actions can be refined without exhaustive enumeration by clustering and filtering.
  • A simple grid-world guess for the target supplies useful reward shaping when sensor data is limited.
  • Theoretical convergence guarantees support reliable deployment beyond the tested office scenes.

Where Pith is reading between the lines

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

  • The same belief-reuse and clustering machinery could apply to other partially observable robotics tasks such as active mapping or search-and-rescue.
  • If perception reliability is the main bottleneck, future work could couple the solver more tightly with uncertainty-aware mapping rather than treating perception as a black box.
  • Real-time performance on Stretch-like platforms suggests the method may scale to longer-horizon household tasks once integrated with existing SLAM systems.

Load-bearing premise

The perception module must supply reliable observations that keep belief updates accurate, and the modified MCTS must converge for growing states and hybrid actions.

What would settle it

Run the same Fetch or Stretch robot, identical perception pipeline, and computational limits in the reported Gazebo environments; if GNPF-kCT does not locate targets faster or with higher success rate than the listed POMDP baselines and LLM-based solvers, the performance claim is false.

Figures

Figures reproduced from arXiv: 2604.14965 by Hanna Kurniawati, Hesheng Wang, Shoudong Huang, Yongbo Chen.

Figure 1
Figure 1. Figure 1: A scenario of object search involves a Fetch robot locating and removing a pink snack box from a table using feature matching. The robot actively adjusts its base, lift, and head (red) to change its view, with no prior knowledge of the size, number, or color of objects on the table. object segmentation, object detection, pose estima￾tion, task-level planning, and action-level planning. Significant advancem… view at source ↗
Figure 2
Figure 2. Figure 2: The main steps in our proposed approach, GNPF-kCT 4 POMDP formulation for object search 4.1 General hybrid and growing POMDP In this paper, we consider a POMDP formulation P with a hybrid action domain. Formally, it is defined as an 8-tuple < S, A, O, T, Z, R, b0, γ >, where the state space S at the k-th time step Sk denotes the set of all possible states of the robot and the environment and is assumed to … view at source ↗
Figure 3
Figure 3. Figure 3: Grid world update in one frame Action space A. Our method is a task-level planning framework designed with multiple prim￾itive actions, rather than control-level planning. These actions adjust the robot camera’s FOV to cover the workspace and enable object manipula￾tion. The action space A comprises 3 primitive actions, including changing the robot configuration ar, which belongs to a continuous action dom… view at source ↗
Figure 4
Figure 4. Figure 4: Robot successfully detects the green grids in this scene and its variable ”scored” will add 1 in Algorithm 2. The inputs to Algorithm 2 include: 1) a 2D occupancy grid map for initializing 3D ICP matching; 2) A 3D point cloud map, employed to create a fused object point cloud by performing scan matching with the current 3D camera’s point cloud and filtering out points outside the workspace; 3) The Fetch si… view at source ↗
Figure 5
Figure 5. Figure 5: The neural process-based scoring function, some scattered clusters, and some samples. 5.3.2 Action selection strategy and list growing in MCTS. Inspired by the classical hierarchical optimistic optimization (HOO) idea, which comes from the continuous-arm bandit problem, we select an action from the set of candidate actions L(nodeo) according to: a ∗ = argmaxa∈L(nodeo)U(nodeo, a), U(nodeo, a) = Qˆ(nodeo, a)… view at source ↗
Figure 6
Figure 6. Figure 6: The refining process using clustered episode IDs. Algorithm 5 Refine(T , nodeo, a ∗ , H) Input: The belief tree T , the belief node nodeo, the selected action a ∗ , the recorded action-observation history list H Output: The updated history list H and the new tree T with the refined nodes 1: Collects all applied actions S ′ r = {ai} in previous episodes passed leaf node of belief node nodeo with its action … view at source ↗
Figure 7
Figure 7. Figure 7: Main steps for growing state space Algorithm 10 Simulation new objects(nodeo, hs, hr, h, i) Input: The previous belief node nodeo, the updated histories in one particle hs, hr, h, the depth value i Output: The updating tree T , the discounted accumulated reward r 1: a ∗ ← h[2 ∗ i] 2: if i == dimr(Hs)[key] then ▷ Reach the terminal node in tree structure and the later part in this history is corresponding t… view at source ↗
Figure 9
Figure 9. Figure 9: Measurement from point cloud ¶For the Stretch robot, which also has a camera and LiDAR but only a 3-DoF manipulator, we adapt to its constraints by using the IKPy tool (Manceron et al., 2022) for inverse kinematics combined with base motion to achieve the required grasp poses. Prepared using sagej.cls [PITH_FULL_IMAGE:figures/full_fig_p018_9.png] view at source ↗
Figure 11
Figure 11. Figure 11: Candidate grasp poses 7 Simulations and real-world experiments 7.1 Simulations We validate our approach using Fetch and Stretch robot simulators in the Gazebo environment, implemented by C++ and Python. The neural process network is trained for 3000 iterations (approximately 4 hours) on a single NVIDIA 3090 GPU. Post-training, we execute our project on a desktop machine, utilizing only the CPU, operating … view at source ↗
Figure 10
Figure 10. Figure 10: Object detector 6.4 Move-ability estimation In real-world settings, some objects may be immovable due to size, manipulator limits, or base motion constraints. Our framework focuses on clearing the FOV by manipulating objects, requiring the estimation of movability probabilities and updating these beliefs within the POMDP planning. Using point cloud segmentation on the fused global cloud, we isolate object… view at source ↗
Figure 13
Figure 13. Figure 13: shows the performance of the trained network after 3000 iterations, comparing observed test data (red line) with the predicted Gaussian distribution’s 2-σ bound (pink area, truncated to [0, 1]). Data are reordered by the robot’s position to highlight trends. Predicted accuracy Acc is defined as: Acc = 1 − P row (13) where P row is the probability of considering efficient actions (observed probability is l… view at source ↗
Figure 14
Figure 14. Figure 14: The planning environment Comparison results Many existing object search methods rely on classical POMCP, which oper￾ates in a discrete action domain. We evaluate our method against both discrete-action POMDP solvers (POMCP, GPOMCP) and continuous￾action solvers (POMCPOW, VOMCPOW, NPF￾kCT). For POMCP and GPOMCP, robot configu￾rations ar are manually limited to combinations of 4 selected poses, 3 lift heigh… view at source ↗
Figure 12
Figure 12. Figure 12: Loss Trends [PITH_FULL_IMAGE:figures/full_fig_p020_12.png] view at source ↗
Figure 16
Figure 16. Figure 16: Good observations based on tricky robot configurations (very common to obtain based on our method). In simpler scenarios like Loose1 and Hidden1, NP filtering and refined clustering enable the robot’s FOV to quickly cover the workspace, often observing all objects in the first step. In these cases, belief tree reuse is less effective or counterproductive due to added complexity (Section 5.4), no new obser… view at source ↗
Figure 15
Figure 15. Figure 15: Occluded observations even under good robot configurations [PITH_FULL_IMAGE:figures/full_fig_p021_15.png] view at source ↗
Figure 17
Figure 17. Figure 17: The visual progress for Covered1 scenario [PITH_FULL_IMAGE:figures/full_fig_p022_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: Results for different nodd values [PITH_FULL_IMAGE:figures/full_fig_p023_18.png] view at source ↗
Figure 21
Figure 21. Figure 21: Results for Loose1 scenario using Stretch robot. 7.2 Real-world experiments [PITH_FULL_IMAGE:figures/full_fig_p023_21.png] view at source ↗
Figure 22
Figure 22. Figure 22: The real-world planning environment [PITH_FULL_IMAGE:figures/full_fig_p023_22.png] view at source ↗
Figure 23
Figure 23. Figure 23: The used maps in real world experiments. We validate our method on the real-world Stretch platform ( [PITH_FULL_IMAGE:figures/full_fig_p023_23.png] view at source ↗
Figure 24
Figure 24. Figure 24: shows the real robot’s action sequence in 5 steps, including pose adjustments, camera orientation changes, and declarations of obstacles and the target object (yellow dashed circle) [PITH_FULL_IMAGE:figures/full_fig_p024_24.png] view at source ↗
Figure 25
Figure 25. Figure 25: Failed and successful real-world primitive actions. 7.3 Comparison with non-POMDP framework All previous results were obtained using our pro￾posed POMDP framework. In this section, we com￾pare our method with several representative non￾POMDP planning frameworks, including Random, NPF-G, SGoLAM (Kim et al., 2021), SayPlan (Rana et al., 2023), and MoMa-LLM (Honerkamp et al., 2024). Since these planning meth… view at source ↗
Figure 26
Figure 26. Figure 26: Architecture of the used network. we have: ∥V ∗ (b(s ′ new : s ′ ⇄ s ′ add)) − V ∗ (b(s ′ new : s ′ → s ′ add))∥1 = ∥ 1 N X N i=1 α(s ′ new)I(s ′ new = s i new ′ : s ′ ⇄ s ′ add) − 1 N X N i=1 α(s ′ new)I(s ′ new = s i new ′ : s ′ → s ′ add)∥1 ≤ ∥ 1 N X N i=1 Rmax 1 − γ I(s ′ new = s i new ′ : s ′ ⇄ s ′ add) − 1 N X N i=1 Rmax 1 − γ I(s ′ new = s i new ′ : s ′ → s ′ add)∥1 (21) = Rmax 1 − γ ∥b(s ′ new : s… view at source ↗
read the original abstract

Efficiently locating target objects in complex indoor environments with diverse furniture, such as shelves, tables, and beds, is a significant challenge for mobile robots. This difficulty arises from factors like localization errors, limited fields of view, and visual occlusion. We address this by framing the object-search task as a highdimensional Partially Observable Markov Decision Process (POMDP) with a growing state space and hybrid (continuous and discrete) action spaces in 3D environments. Based on a meticulously designed perception module, a novel online POMDP solver named the growing neural process filtered k-center clustering tree (GNPF-kCT) is proposed to tackle this problem. Optimal actions are selected using Monte Carlo Tree Search (MCTS) with belief tree reuse for growing state space, a neural process network to filter useless primitive actions, and k-center clustering hypersphere discretization for efficient refinement of high-dimensional action spaces. A modified upper-confidence bound (UCB), informed by belief differences and action value functions within cells of estimated diameters, guides MCTS expansion. Theoretical analysis validates the convergence and performance potential of our method. To address scenarios with limited information or rewards, we also introduce a guessed target object with a grid-world model as a key strategy to enhance search efficiency. Extensive Gazebo simulations with Fetch and Stretch robots demonstrate faster and more reliable target localization than POMDP-based baselines and state-of-the-art (SOTA) non-POMDP-based solvers, especially large language model (LLM) based methods, in object search under the same computational constraints and perception systems. Real-world tests in office environments confirm the practical applicability of our approach. Project page: https://sites.google.com/view/gnpfkct.

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

1 major / 2 minor

Summary. The paper frames object search in complex 3D indoor environments as a high-dimensional POMDP with growing state space and hybrid continuous-discrete actions. It introduces the GNPF-kCT online solver that combines MCTS with belief-tree reuse, a neural-process network to filter primitive actions, k-center clustering for hypersphere discretization of the action space, and a modified UCB that incorporates belief differences and action values within estimated-diameter cells. A guessed-target grid-world strategy is added for low-information cases. Theoretical analysis is stated to validate convergence, while Gazebo simulations on Fetch and Stretch robots plus real-world office tests claim faster and more reliable localization than POMDP baselines and SOTA non-POMDP solvers (including LLM-based methods) under identical computational and perception constraints.

Significance. If the performance gains and convergence guarantees hold, the work would provide a concrete engineering advance for practical POMDP solving in robotics by addressing growing states and hybrid actions through the specific combination of neural filtering and clustering-based discretization. The dual simulation-plus-real-world evaluation under matched constraints is a strength, as is the public project page for reproducibility.

major comments (1)
  1. [Theoretical Analysis] Theoretical Analysis section: The central claim that GNPF-kCT reliably outperforms baselines rests on convergence of the modified MCTS under growing states and hybrid 3D actions. The paper asserts that k-center hypersphere discretization plus the modified UCB (informed by belief differences and cell diameters) guarantees this, yet no explicit error bounds are supplied showing that the neural-process filter or belief-tree reuse preserve the diameter-controlled approximation error. If these components violate the discretization assumptions, standard MCTS regret arguments cease to apply and the claimed convergence is unsupported. A proof sketch or quantitative bound relating the clustering diameters to the hybrid action space and growing-state reuse is required.
minor comments (2)
  1. [Abstract] Abstract: Performance claims are stated qualitatively ('faster and more reliable') without any numerical metrics, specific baseline names, or forward references to tables or figures that would allow immediate assessment of effect sizes.
  2. [Method] The guessed-target grid-world strategy is introduced to improve efficiency in low-information regimes, but no analysis is given of whether it can introduce new failure modes (e.g., persistent belief bias) under the same perception system used for the main POMDP.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive and detailed review. The feedback on the theoretical analysis is particularly valuable, and we agree that strengthening this section will improve the manuscript. We provide a point-by-point response to the major comment below and will revise accordingly.

read point-by-point responses

  1. Referee: [Theoretical Analysis] Theoretical Analysis section: The central claim that GNPF-kCT reliably outperforms baselines rests on convergence of the modified MCTS under growing states and hybrid 3D actions. The paper asserts that k-center hypersphere discretization plus the modified UCB (informed by belief differences and cell diameters) guarantees this, yet no explicit error bounds are supplied showing that the neural-process filter or belief-tree reuse preserve the diameter-controlled approximation error. If these components violate the discretization assumptions, standard MCTS regret arguments cease to apply and the claimed convergence is unsupported. A proof sketch or quantitative bound relating the clustering diameters to the hybrid action space and growing-state reuse is required.

    Authors: We appreciate the referee's careful reading and agree that the current theoretical analysis would benefit from more explicit quantitative bounds. The manuscript's Theoretical Analysis section provides a high-level argument that the k-center hypersphere discretization combined with the modified UCB (incorporating belief differences and estimated cell diameters) extends standard MCTS convergence properties to the growing-state and hybrid-action setting. However, we acknowledge that detailed error bounds explicitly addressing preservation of the diameter-controlled approximation under neural-process filtering and belief-tree reuse are not supplied. In the revised manuscript, we will expand this section with a proof sketch that: (1) bounds the perturbation introduced by the neural-process filter, showing that filtered primitive actions remain within the same hyperspheres and thus preserve the diameter-based discretization error; (2) demonstrates that belief-tree reuse maintains the approximation guarantees via incremental belief updates that do not increase the effective state-space diameter beyond the clustering tolerance; and (3) derives a modified regret bound for the hybrid continuous-discrete action space by relating clustering diameters to the continuous action components and discrete mode switches. This will rigorously connect the components to the overall convergence claim without altering the core algorithmic contributions. revision: yes

Circularity Check

0 steps flagged

No significant circularity; derivation uses standard MCTS components with independent theoretical validation

full rationale

The paper frames object search as a POMDP and introduces GNPF-kCT as a solver combining MCTS with belief reuse, neural process filtering, and k-center discretization. The abstract states that theoretical analysis validates convergence, but this analysis is presented as internal to the paper rather than reducing any claim to a self-referential fit or prior self-citation. Performance is demonstrated via external Gazebo simulations and real-world tests against baselines, not by construction equivalent to the method's own inputs. No load-bearing step matches the enumerated circularity patterns; the central claims remain independent of self-definition or renamed empirical patterns.

Axiom & Free-Parameter Ledger

2 free parameters · 2 axioms · 2 invented entities

Ledger is necessarily approximate because only the abstract is available; full details on hyperparameters, proof assumptions, and implementation choices are absent.

free parameters (2)
  • k-center clustering diameters and number of centers
    Used to discretize high-dimensional hybrid action spaces; values are not stated in the abstract and are likely tuned for the reported experiments.
  • neural process network hyperparameters
    Filter for useless primitive actions; training or architecture details are not provided.
axioms (2)
  • domain assumption Object search in 3D indoor environments with localization error and occlusion can be faithfully represented as a POMDP with growing state space and hybrid actions
    The framing is adopted without discussion of Markov property violations or observability model adequacy.
  • domain assumption Monte Carlo Tree Search with belief reuse converges under the modified UCB rule for this growing-state setting
    Theoretical analysis is asserted but not detailed in the abstract.
invented entities (2)
  • GNPF-kCT solver no independent evidence
    purpose: Online solver for high-dimensional POMDPs with growing states and hybrid actions
    Newly proposed architecture whose independent validation rests on the claimed experiments.
  • guessed target object with grid-world model no independent evidence
    purpose: Fallback strategy to maintain search progress when information or reward is limited
    Introduced heuristic whose benefit is asserted but not independently falsifiable from the abstract.

pith-pipeline@v0.9.0 · 5618 in / 1721 out tokens · 50498 ms · 2026-05-10T10:40:56.222834+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

24 extracted references · 24 canonical work pages

  1. [6]

    Output Response Format: Analysis:describe where you could find the objects of interest and what actions you need to execute to get there

    done(): call when the task (target object is removed) is completed or if you are unable to take any further actions. Output Response Format: Analysis:describe where you could find the objects of interest and what actions you need to execute to get there. Reasoning:justify why the next action is important to solve the task. Command:function call’, ’role’: ...

    work page

  2. [10]

    If the updating object reaches the obstacle or target index, call the claim action first

    To observe objects from different directions first to get the belief update until reach obstacle or target index. If the updating object reaches the obstacle or target index, call the claim action first. If it is claimed as a target object (greatly welcome), directly call the remove action and then be done. Task completed and you do not need to navigate to it

    work page

  3. [11]

    If an object is likely to be the target object, it is good to navigate to this object and observe it from different directions for later claim action

    work page

  4. [15]

    object 3

    You can’t explore rooms that haven’t been found yet. LLM prompts example for low-level navigation action {Content:This is the response result in this step: **Analysis:** The target object "object 3" has been successfully identified in room-0. The next best action would be to navigate to the updating object "object 4" and observe it from different directio...

    work page

  5. [17]

    } LLM prompts example for MoMa-LLM Content:You are a Fetch robot in an unexplored house

    Make sure that the output vector only includes 6 numbers corresponding to the position (x axis), the position (y axis), orientation, pan joint, tilt joint, lift joint and divided by ;. } LLM prompts example for MoMa-LLM Content:You are a Fetch robot in an unexplored house. Your task is to find and remove a blue snack box, its corresponding property is lab...

    work page

  6. [23]

    Output Response Format: Analysis:describe where you could find the objects of interest and what actions you need to execute to get there

    done(): call when the task (target object is removed) is completed or if you are unable to take any further actions. Output Response Format: Analysis:describe where you could find the objects of interest and what actions you need to execute to get there. Reasoning:justify why the next action is important to solve the task. Prepared usingsagej.cls Yongbo C...

    work page

  7. [30]

    If some actions are repeated several times, they may not be possible and you need to observe that object from more directions using the explore action

    work page

  8. [31]

    Directly removing is illegal

    Each object needs to be claimed as the target or obstacle object first and then it is legal to remove it. Directly removing is illegal

    work page

  9. [32]

    Your task is to find and remove a blue snack box, its corresponding property is labeled as target object based on your head camera

    You can’t explore rooms that haven’t been found yet.’}, LLM prompts example for MoMa-LLM with more geometry properties Content:You are a Fetch robot in an unexplored house. Your task is to find and remove a blue snack box, its corresponding property is labeled as target object based on your head camera. You have the coordinates of the four vertices of eac...

    work page

  10. [33]

    navigate(room name, object name): navigate to a configuration (pose and joint angles) that can observe this object in this room

    work page

  11. [34]

    claim to obstacle(room name, object name): claim this object is the obstacle object, when the object has a high possibility to be the obstacle object

    work page

  12. [35]

    claim to target(room name, object name): claim this object is the target object, when the object has a high possibility to be the obstacle object

    work page

  13. [36]

    remove(room name, object name): remove this target or obstacle object to complete task or free some field of view for better exploration

    work page

  14. [37]

    explore(room name): randomly select a frontier to explore the unknown space near one of the rooms that is not fully explored yet

    work page

  15. [38]

    Output Response Format: Analysis:Describe where you could find the objects of interest and what actions you need to execute to get there

    done(): call when the task (target object is removed) is completed or if you are unable to take any further actions. Output Response Format: Analysis:Describe where you could find the objects of interest and what actions you need to execute to get there. Reasoning:justify why the next action is important for solving the task. Content:’You are currently in...

    work page

  16. [39]

    Respond with a function call

    work page

  17. [40]

    Object names have to match the description exactly

    You can only use the objects and rooms that you have already found. Object names have to match the description exactly

    work page

  18. [41]

    The explore action allows the robot to adjust its configuration, change its field of view, and make an initial observation of the object

    You can only explore the found rooms that contain unexplored areas. The explore action allows the robot to adjust its configuration, change its field of view, and make an initial observation of the object. This first observation is essential, as it provides the basis for updating the object and reaching its index

    work page

  19. [42]

    If the updating object reaches the obstacle or target index, call the claim action first

    To observe objects from different directions first to get the belief update until reach obstacle or target index. If the updating object reaches the obstacle or target index, call the claim action first. If it is claimed as a target object (greatly welcome), directly call remove action and then done. Task completed and You do not need to navigate to it

    work page

  20. [43]

    If an object is likely to be target object, it is good to navigate to this object and observe it from different directions for later claim action

    work page

  21. [44]

    After an object is claimed as the obstacle object, it is legal to remove it to free some field of view

    The claim of both the target and obstacle objects is encouraged. After an object is claimed as the obstacle object, it is legal to remove it to free some field of view. However, the removal action often fails in real-world settings, so obstacle removal should be used cautiously and only attempted occasionally

    work page

  22. [45]

    If some actions are repeated several times, they may not be possible and you need to observe that object from more directions using explore action

    work page

  23. [46]

    Directly remove is illegal

    Each object needs to be claimed as the target or obstacle object first and then it is legal to remove it. Directly remove is illegal

    work page

  24. [47]

    You can’t explore rooms that haven’t been found yet.’, Prepared usingsagej.cls

    work page