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REVIEW 2 major objections 8 minor 1 cited by

ManiTaskGen: A Comprehensive Task Generator for Benchmarking and Improving Vision-Language Agents on Embodied Decision-Making

T0 review · 2 major / 8 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read ManiTaskGen claims the full space of feasible mobile-manipulation tasks in a scene can be generated automatically from a receptacle-aware scene graph, yielding near-exhaustive benchmarks and training tasks.

desk verdict A genuinely useful pick-and-place task generator with a solid 40K dataset, but the near-exhaustiveness claim overreaches beyond the formalism; still deserves review. read the letter →

arxiv 2505.20726 v2 pith:E7KJ6E62 submitted 2025-05-27 cs.RO

Liu Dai , Haina Wang , Weikang Wan , Hao Su This is my paper
classification cs.RO
keywords mobilemanipulationtaskgenerationreceptacle-awarescenegraphvision-languagemodelsembodieddecision-makingautomaticbenchmarkconstructionpick-and-placetasksoutcome-basedtemplates
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper introduces ManiTaskGen, a generator that takes object poses, bounding boxes, and optionally mesh models, and automatically produces a large, diverse set of feasible mobile manipulation tasks for any scene. It argues that the agent-environment interaction cycle, where each atomic object relocation updates the scene state and determines the next feasible actions, defines the space of all possible tasks for an agent with navigate, grasp, and place skills. From three scenes the system curates 39,871 tasks, with human-verified validity rates of 94.0% for process-based instructions and 86.5% for outcome-based instructions, well above a GPT-based baseline. The same generated tasks are used to build an automatic benchmark, where current vision-language agents succeed on fewer than 30% of episodes, and an inference-time self-reflection loop that raises success rates by 10 to 22 percentage points. If the claims hold, scene diversity no longer has to be capped by manual task annotation, and any new scene can become a source of evaluation and improvement data.

What carries the argument

The load-bearing object is the Receptacle-Aware 3D Scene Graph, a containment tree of scene objects whose free surface areas are segmented into anchor-relative directional receptacle regions, with empty surfaces divided into a 3x3 grid and adjacent regions mergeable into larger ones. It carries the argument by making the set of feasible atomic actions enumerable: every object-receptacle pair with sufficient space yields a candidate relocation, and simulating those relocations produces the state transitions needed to chain multi-step process tasks. The same graph supplies the platforms and objects that instantiate outcome-based task templates, while a 200-template curated dataset combined with VLM voting provides the abstract instruction coverage.

What would settle it

Take a scene with a closed drawer, an unlit lamp, and a loaf of bread with a knife; generate ManiTaskGen's full task set for it and check every instruction. If no generated instruction requires a non-pose state change, such as switching on, opening, pouring, or cutting, then the task set is not near-exhaustive over what a capable embodied agent could do in that scene.

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Extended reading notes

Core claim

The central claim is that the task space for an embodied mobile manipulation agent with navigate, grasp, and place primitives is captured by the discrete interaction cycle: at each step the scene state, defined by object poses and containment states, restricts the set of feasible atomic actions, defined as parameterized object relocations. All tasks are then either feasible sequences of such actions, called process-based tasks, or reachable target states, called outcome-based tasks. ManiTaskGen operationalizes this by constructing a Receptacle-Aware 3D Scene Graph that segments free surface space into directional receptacle regions, enumerating every feasible object-to-receptacle relocation, and chaining these relocations through simulated state updates to generate multi-step instructions. Outcome-based tasks come from instantiating a set of 200 human-derived templates and filtering with a multi-model VLM vote. The authors report automatic generation of 39,871 tasks from three scenes, validity rates of 94.0% and 86.5%, low average success for current VLMs on the resulting benchmark, and measurable gains from self-reflection using the generated tasks.

Load-bearing premise

Near-exhaustiveness holds only if every feasible task can be expressed as relocating objects between receptacle regions; non-pose changes like toggling, opening, pouring, or cutting are outside the generated space, as the paper's own limitations state.

Editorial extensions

If this is right

  • Any new scene with object poses, bounding boxes, and optionally meshes can be converted into a large benchmark without manual annotation, so task diversity no longer scales with human effort.
  • Process-based tasks come with automatically checkable success conditions, enabling both final and intermediate-step scoring for embodied agents.
  • Current vision-language agents succeed on under 30% of sampled generated tasks, with wrong placement the dominant failure, giving a concrete target for spatial-reasoning progress.
  • An inference-time self-reflection loop that uses generated tasks and their automatic evaluations improved success rates by 10 to 22 percentage points and transferred from one scene to another.
  • Outcome-based tasks add abstract instruction diversity such as clearing or organizing a surface, although automatic evaluation of those abstract tasks is left to future work.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Editorial extension: the interaction-cycle argument generalizes; if atomic actions were extended to articulation and state toggles, the same graph-based enumeration would define a larger task space that remains near-exhaustive by the paper's own logic.
  • Editorial extension: the automatic success predicates on process-based tasks turn the generator into a reward source, so generated tasks could support supervised or reinforcement fine-tuning of policies, not just inference-time reflection.
  • Editorial extension: because Level 4 outcome tasks are not yet benchmarked, a human-judgment or learned-reward protocol is the natural next step, and the paper's template dataset makes that comparison reproducible.
  • Editorial extension: a completeness measure could be defined as the fraction of reachable distinct scene states represented by generated tasks, allowing near-exhaustiveness to be quantified beyond the 40K sample.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 8 minor

Summary. ManiTaskGen is a system that, given scene information (object poses, bounding boxes, and optionally mesh models), automatically generates mobile manipulation task instructions. It constructs a Receptacle-Aware 3D Scene Graph, enumerates feasible atomic pick-and-place actions as object-to-receptacle relocations, chains them into process-based tasks (single- and two-step with THEN/AND/OR connectors), and instantiates 200 human-curated templates into outcome-based tasks filtered by a GPT-4o/Gemini-2.5-pro/Claude-3.7-sonnet voting ensemble. The paper evaluates the generated tasks on three scenes (ReplicaCAD, AI2THOR, SUN-RGBD), reporting human-verified validity rates of 94.0% and 86.5% for process- and outcome-based tasks versus 29.4% and 21.1% for a GPT-4o baseline; benchmarks nine VLM agents in a simulator, finding average success rates below 30% on process-based tasks; and proposes an inference-time self-reflection method that improves three VLMs. The central claim is that the generated task collection is comprehensive and logically near-exhaustive for a given scene.

Significance. If appropriately scoped, this is a useful contribution: it replaces manual task annotation with an automated pipeline, enables large-scale benchmarks, and provides a route to training data for embodied VLMs. Concrete strengths include: (1) the generation mechanism for process-based tasks is deterministic and transparent given the scene graph, making it reproducible and independent of the measured outcomes; (2) the human validity check is a genuine comparative result, beating the GPT-4o baseline by a large margin; (3) the benchmark harness provides automatic, precise success verification for Levels 1–3; and (4) the improvement experiment (Table 4) demonstrates concrete cross-scene transfer. The main caveat is that the “near-exhaustive” claim is only defensible within the class of tasks achievable by navigate/pick/place primitives over pose/containment states; as stated in the abstract and introduction, it overreaches. With that claim re-scoped, the framework would be a valuable tool for scaling benchmark and training-task creation across arbitrary scenes.

major comments (2)
  1. [Sec. 1; Sec. 3.1; Sec. 6] The paper's headline claim — that ManiTaskGen generates task collections that are “logically near-exhaustive” for a scene (Sec. 1) and cover “the full spectrum of feasible tasks within any given scene” (Abstract) — is not supported by the formalization in Sec. 3.1. There, the scene state is defined solely by object poses and containment states, and atomic actions are parameterized object relocations; the assertion that this interaction cycle “inherently defines the space of all possible tasks” (Sec. 3.1 and Fig. 2) is a definitional choice rather than a derived result, and no argument is given that every feasible mobile manipulation task reduces to object relocation. Feasible tasks such as turning on a lamp, opening a drawer, or pouring from a pitcher are not expressible in this formalism, and Sec. 6 confirms that non-pose state changes and fine-grained manipulation are future work. Because near-exhaustiveness is the paper's primary novelty and scaling argument, the abstract and introduction should be re-scoped to “near-exhaustive within the class of tasks achievable by navigate/pick/place primitives,” with that caveat carried consistently into the conclusion.
  2. [Sec. 4, Table 2] The human-verified validity rates in Table 2 are the principal evidence for the feasibility of the generated tasks, but they are reported as bare point estimates: no sample size, no confidence interval, no annotator agreement, and no description of how tasks or annotators were selected for verification. With an unknown N, a rate of 94.0% could range from 47/50 to 940/1000, and the difference from the 29.4% baseline cannot be assessed for statistical significance; without agreement measures, the objectivity of the labeling is unverifiable. Please report per-condition sample sizes, the number of annotators and their agreement (e.g., Cohen's kappa), the sampling procedure (e.g., random stratified by level), and confidence intervals around the validity rates.
minor comments (8)
  1. [Sec. 3.3] There is a duplicated word: “explicitly encodes the the properties of every object” should read “the properties.”
  2. [Sec. 5.1] “The benchmark is constructed within an simulators [43]” is ungrammatical; it should be “within a simulator” or “within simulators.”
  3. [Sec. 4, Fig. 4] The diversity comparison is based on 100 sampled tasks and presented as qualitative “lightmap” visualizations; a quantitative diversity metric (e.g., number of unique objects and receptacles covered per 100 tasks, with the sampling procedure and variability across samples) would make the diversity claim testable.
  4. [Sec. 5.1.2] Level 4 (outcome-based) tasks are excluded from the benchmark (“We leave benchmarking Level 4 tasks in future work”), yet the abstract says the framework “thoroughly evaluat[es]” agents; the abstract and introduction should state that the reported evaluation covers only the process-based levels.
  5. [Sec. 5.2.2, Table 4] The improvement results are based on 10 trial episodes and 100 Level-1 test tasks; please state whether the numbers are single runs or averages over seeds, and add variance or per-seed results so the improvements can be interpreted.
  6. [Table 1] Comparing total task counts across datasets with very different numbers of scenes (e.g., M3Bench with 119 scenes versus 3 scenes here) conflates scale with density, and the “+∞” entry for ManiTaskGen-RAS is not a meaningful comparison to finite dataset sizes.
  7. [Appendix D.2] The failed-episode transcript is internally inconsistent with the successful-episode transcript: the task description shown at “Steps used: 1/20” is “Move kitchenware_white_paper_towel_holder…” although the episode's stated task is “Move Remote to Bowl's rear-left receptacles,” and the action names differ across the two transcripts (show_receptacle_of_object vs. show_receptacles_of_object; change_view vs. rotate_observation_view_of_current_platform); these transcripts appear to be taken from different runs and should be made consistent.
  8. [Sec. 4, Appendix C.2] The outcome-based task filter uses an ensemble of GPT-4o, Gemini-2.5-pro, and Claude-3.7-sonnet — the same model families later benchmarked in Sec. 5.1; the impact is limited because the benchmark covers only Levels 1–3, but the overlap should be explicitly acknowledged, and the filter's acceptance rate (how many template instantiations were rejected) should be reported.

Circularity Check

1 steps flagged · score 2.0 of 10

Near-exhaustiveness is a definitional consequence of restricting scene state to poses/containment and atomic actions to object relocation; the benchmark and validity results remain independent.

  1. self definitional [Sec. 3.1 (Premise and Formalization), incl. Fig. 2 caption; cf. Sec. 6 Limitations]
    "Based on the agent’s core capabilities, we define an atomic action a ∈ A as a parameterized object relocation operation. ... This cycle, where executing atomic actions (e.g., single object relocation) updates the scene state and determines subsequent available actions, inherently defines the space of all possible tasks."

    The 'logically near-exhaustive' claim is not derived from an independent characterization of mobile manipulation; it is a restatement of the paper's own state/action definitions. Scene state is restricted to object poses plus containment states, and atomic actions are restricted to parameterized relocations, so 'all possible tasks' means all tasks in the relocation-only space by construction. Feasible tasks that change non-pose state (turn on a lamp, open a drawer, pour, cut) are excluded by the state and action vocabulary, and the paper's Limitations section concedes that non-pose state changes are future work.

full rationale

ManiTaskGen's core generation pipeline—scene graph construction, receptacle segmentation, sampling of feasible relocation sequences, and human/template-based outcome tasks—is not fitted to the later benchmark outcomes. Human-verified validity (94.0% process, 86.5% outcome) and the VLM benchmark results are externally evaluated, and the improvement experiments explicitly avoid leakage by training on ReplicaCAD and testing on AI2THOR. No load-bearing self-citation chain or uniqueness theorem is invoked. The only definitional circularity is the near-exhaustiveness claim: because the formalization defines the task space as reachable object-relocation states and sequences, the generated task set is comprehensive for that space by construction. This is a scope stipulation, not a discovered property of all mobile manipulation tasks, and the paper's own limitation statement admits non-pose manipulation is excluded. The VLM voting for outcome tasks uses the same model families that later appear in the benchmark, but Level 4 benchmarking is explicitly deferred, so no measured performance claim is derived from that filter; it is therefore not counted as a fitted-input prediction.

Assumptions & free parameters 7 free parameters · 7 assumptions · 0 invented entities

The paper introduces no fitted numerical model, but the system rests on a hand-built task taxonomy, a 200-template human dataset, and several hand-set thresholds. The near-exhaustive claim is an assumption about the interaction cycle rather than a proven result; no new physical entities are postulated.

free parameters (7)
  • walkable_space_threshold_50cm = 50 cm
    Defines which unobstructed space around a ground object counts as walkable (Appendix B.1); directly shapes the navigation action space and therefore the generated tasks.
  • empty_surface_grid_3x3 = 3x3 grid
    Empty platform surfaces are split into nine rectangular receptacle regions (Appendix B.2.1); this discretization determines placement targets and direction labels.
  • directional_receptacles_8 = 8 regions per anchor object
    Free space around each object is split into four cardinal and four diagonal receptacles (Appendix B.2); the granularity controls how many distinct 'move near X' tasks exist.
  • vlm_vote_threshold = 2 of 3 VLMs
    An outcome task is kept only if at least two of GPT-4o, Gemini-2.5-pro, and Claude-3.7-sonnet label it feasible (Appendix C.2); the cutoff trades validity against diversity.
  • receptacle_merge_limit = up to 4 regions
    The generator merges at most four adjacent or overlapping receptacle regions when checking whether an object fits (Appendix B.2.3); affects feasibility of large-object tasks.
  • level3_task_counts = 5,000 / 5,000 / 500 per scene
    Two-step Level 3 tasks were sampled 5,000 each for ReplicaCAD and AI2THOR and 500 for SUN-RGBD (Appendix D.1.2); a scale decision, not fitted.
  • episode_timestep_limits = 20 (Levels 1-2), 40 (Level 3)
    Episode budgets in Sec. 5.1.3; longer horizons are expected to lower success rates, so this is a meaningful benchmark choice.
assumptions (7)
  • domain assumption The task space is captured by the process/outcome dichotomy based on the agent-environment interaction cycle.
    Sec. 3.1 and Fig. 2; this grounds the 'logically near-exhaustive' claim. Non-pose changes are excluded (Sec. 6).
  • domain assumption Agent primitives are limited to navigate, pick, and place, and atomic actions are single-object relocations.
    Sec. 3.1; ManiTaskGen generates only tasks solvable by these primitives, not arbitrary manipulation.
  • domain assumption Input scene information (object poses, bounding boxes, optionally meshes) is accurate and sufficient.
    Sec. 3.2; no perception noise is modeled, so real-world applicability depends on upstream perception quality.
  • domain assumption Ground objects are immovable and only surface objects are manipulable.
    Appendix B.1; this simplification decides which objects appear as movable in generated tasks.
  • domain assumption MANI TASKOT-200 is a representative template basis for outcome-based tasks.
    Appendix C.1; the abstract task coverage is inherited from 200 human-written templates, not derived from scene structure.
  • domain assumption VLM ensemble votes are a reliable proxy for outcome-task feasibility.
    Appendix C.2; no external feasibility ground truth is available beyond small human spot checks.
  • domain assumption The human validity sample is representative of the 39,871-task dataset.
    Sec. 4, Table 2; sample size, annotator count, and agreement are not reported.

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Cite this review

Pith. "Pith review of ManiTaskGen: A Comprehensive Task Generator for Benchmarking and Improving Vision-Language Agents on Embodied Decision-Making." pith.science (2026). https://pith.science/paper/E7KJ6E62

@misc{pith2026250520726,
  author       = {Pith},
  title        = {Pith review of: ManiTaskGen: A Comprehensive Task Generator for Benchmarking and Improving Vision-Language Agents on Embodied Decision-Making},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/E7KJ6E62}},
  note         = {Machine review of arXiv:2505.20726}
}
read the original abstract

Building embodied agents capable of accomplishing arbitrary tasks is a core objective towards achieving embodied artificial general intelligence (E-AGI). While recent work has advanced such general robot policies, their training and evaluation are often limited to tasks within specific scenes, involving restricted instructions and scenarios. Existing benchmarks also typically rely on manual annotation of limited tasks in a few scenes. We argue that exploring the full spectrum of feasible tasks within any given scene is crucial, as they provide both extensive benchmarks for evaluation and valuable resources for agent improvement. Towards this end, we introduce ManiTaskGen, a novel system that automatically generates comprehensive, diverse, feasible mobile manipulation tasks for any given scene. The generated tasks encompass both process-based, specific instructions (e.g., "move object from X to Y") and outcome-based, abstract instructions (e.g., "clear the table"). We apply ManiTaskGen to both simulated and real-world scenes, demonstrating the validity and diversity of the generated tasks. We then leverage these tasks to automatically construct benchmarks, thoroughly evaluating the embodied decision-making capabilities of agents built upon existing vision-language models (VLMs). Furthermore, we propose a simple yet effective method that utilizes ManiTaskGen tasks to enhance embodied decision-making. Overall, this work presents a universal task generation framework for arbitrary scenes, facilitating both benchmarking and improvement of embodied decision-making agents.

Figures

Figures reproduced from arXiv: 2505.20726 by the authors.

Figure 1
Figure 1. Framework Overview. ManiTaskGen is a universal system that generates a comprehensive set of feasible mobile manipulation tasks given arbitrary scene. These tasks facilitate automatic benchmarking and the improvement of embodied decision-making agents. a few scenes and authoring a limited number of scene-specific tasks related to objects within those scenes, which serve as training or testing objectives for embodied … view at source ↗
Figure 2
Figure 2. Agent-Env Interaction Cycle. This cycle provides a fundamental depiction of how an embodied agent executes a task within a scene. Accordingly, we categorize all tasks into two dis￾tinct types: process- and outcome-based tasks. This cycle, where executing atomic actions (e.g., single object relocation) updates the scene state and determines subsequent available actions, in￾herently defines the space of all possible t… view at source ↗
Figure 3
Figure 3. Visualization of Segmented Recepta￾cle Regions on a Surface. After that, we identify the available receptacles within the scene. Building upon the established ob￾ject relationship tree, we characterize receptacles by segmenting the free space of surfaces. Specif￾ically, we treat each object as an anchor, and seg￾ment the free space around it into eight directional receptacle regions, with boundaries extending un￾til… view at source ↗
Figures from the paper (26 more)
Figure 4
Figure 4. Figure 4: The "lightmap"s Which Show the Diversity of Generated Tasks. Each time an object or location is mentioned in a task, we add a highlight at the corresponding position. The brightness intensity and distribution reveal that our method generates tasks covering more objects…
Figure 5
Figure 5. Figure 5: Visualization of An Example Testing Episode. The agent is equipped with abstracted navigation (go_to, change_view), grasping (pick) and placing (show_receptacle, place) skills. Blue marks indicate the walkable locations around ground objects involved in this episode. a…
Figure 6
Figure 6. Figure 6: Success and Failure Modes from Gemini-2.5-pro in each stage of Level 3 tasks. We evaluate existing VLM-based embodied agents in the simulated scenes [23, 24] from ManiTaskGen-RAS-40K. Specifically, we ran￾domly sample 1000 tasks for each level of the tasks. A timestep …
Figure 7
Figure 7. Figure 7: Improvement of Gemini-2.5-flash Agent. We find that more trial episodes continu￾ously contribute to the agent performance. To ensure no data leakage between optimiza￾tion and deployment, we select tasks from the ReplicaCAD [23] scene of ManiTaskGen-RAS as source trial …
Figure 8
Figure 8. Figure 8: Visualization of Rules Involved in Scene Graph Construction. B.2 Calculating Receptacle Regions We construct accurate receptacle region retrieval information in three steps. First, we compute an initial set of rough receptacles based on empty surfaces or the spatial re…
Figure 9
Figure 9. Figure 9: Visualization of the 3 Scenes Used to Generate ManiTaskGen-RAS-40K. C.2 VLM-based Voting Mechanism As described in Sec. 3.3, the process of generating outcome-based tasks involves 2 steps. Firstly, we fill the templates from MANITASKOT-200 with valid combinations of pl…
Figure 10
Figure 10. Figure 10: Ambiguous Item Naming in ReplicaCAD. Item Renaming. We observe that the original object naming in certain input scenes (e.g., ReplicaCAD and AI2THOR) is somewhat casual. As shown in [PITH_FULL_IMAGE:figures/full_fig_p021_10.png]
Figure 11
Figure 11. Figure 11: Images Given at the Step 0 of the Successful Episode [PITH_FULL_IMAGE:figures/full_fig_p024_11.png]
Figure 12
Figure 12. Figure 12: Images Given after Step 1 of the Successful Episode [PITH_FULL_IMAGE:figures/full_fig_p024_12.png]
Figure 13
Figure 13. Figure 13: Images Given after Step 2 of the Successful Episode. 24 [PITH_FULL_IMAGE:figures/full_fig_p024_13.png]
Figure 14
Figure 14. Figure 14: Images Given after Step 3 of the Successful Episode [PITH_FULL_IMAGE:figures/full_fig_p025_14.png]
Figure 15
Figure 15. Figure 15: Images Given after Step 4 of the Successful Episode [PITH_FULL_IMAGE:figures/full_fig_p025_15.png]
Figure 16
Figure 16. Figure 16: Images Given after Step 5 of the Successful Episode [PITH_FULL_IMAGE:figures/full_fig_p025_16.png]
Figure 17
Figure 17. Figure 17: Images Given after Step 6 of the Successful Episode [PITH_FULL_IMAGE:figures/full_fig_p025_17.png]
Figure 18
Figure 18. Figure 18: Images Given after Step 7 of the Successful Episode [PITH_FULL_IMAGE:figures/full_fig_p025_18.png]
Figure 19
Figure 19. Figure 19: Images Given after Step 8 of the Successful Episode. 25 [PITH_FULL_IMAGE:figures/full_fig_p025_19.png]
Figure 20
Figure 20. Figure 20: Images Given after Step 9 of the Successful Episode [PITH_FULL_IMAGE:figures/full_fig_p026_20.png]
Figure 21
Figure 21. Figure 21: Images Given after Step 10 of the Successful Episode [PITH_FULL_IMAGE:figures/full_fig_p026_21.png]
Figure 22
Figure 22. Figure 22: Images Given after Step 11 of the Successful Episode [PITH_FULL_IMAGE:figures/full_fig_p026_22.png]
Figure 23
Figure 23. Figure 23: Images Given after Step 12 of the Successful Episode [PITH_FULL_IMAGE:figures/full_fig_p026_23.png]
Figure 24
Figure 24. Figure 24: Images Given after Step 13 of the Successful Episode. 26 [PITH_FULL_IMAGE:figures/full_fig_p026_24.png]
Figure 25
Figure 25. Figure 25: Images Given after Step 1 of the Failed Episode [PITH_FULL_IMAGE:figures/full_fig_p057_25.png]
Figure 26
Figure 26. Figure 26: Images Given after Step 2 of the Failed Episode [PITH_FULL_IMAGE:figures/full_fig_p057_26.png]
Figure 27
Figure 27. Figure 27: Images Given after Step 3 of the Failed Episode. Unsuccessful Episode by Gemini-2.5-pro System Message:You are a household assistant robot equipped with a vision system and manipulation capabilities. You need to complete a task in a 3D environment, mainly involving mo…
Figure 28
Figure 28. Figure 28: Images Given after Step 4 of the Failed Episode [PITH_FULL_IMAGE:figures/full_fig_p058_28.png]
Figure 29
Figure 29. Figure 29: Images Given After Step 5 of the Failed Episode. - move an object to a platform. - move an object to the dir of a platform. - move an object around another object. - move an object to the dir of another object. - move an object between another object and yet another o…

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Exploratory, Communicative, and Deployable: Vision-Driven Embodied Agents for Open-World Mobile Manipulation

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    REAL, a benchmark and trained vision-language agent for oracle-free mobile manipulation with user interaction, achieves 78.3% end-to-end success on 60 physical-robot episodes after simulation-only high-level training.

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Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.