Embodied Playground: A Long-Horizon Agentic Robotics Benchmark for Physical, Strategic, and Spatial Reasoning

Abstract: Foundation models offer a promising basis for robotic agents, but long-horizon tasks require them to sustain coherent reasoning and reliable physical execution across interdependent stages. We introduce Embodied Playground, a benchmark for evaluating how foundation-model agents coordinate physical, strategic, and spatial reasoning throughout such tasks. Agents must track task progress, satisfy intermediate constraints, and revise their plans using execution feedback as earlier actions shape subsequent decisions. The playground comprises 410 task environments spanning basic manipulation, sequential puzzles, and multi-stage household activities, with variations in planning depth, geometric constraints, and physical execution demands. We curate EP-100, a representative selection of 100 tasks across five suites and 16 task types, together with the EP-Easy and EP-Hard difficulty splits and the compact EP-20 selection. A shared protocol specifies observation and tool access, resource budgets, repeated trials, and independent checks of goal completion, task-rule compliance, and physical validity. Embodied Playground provides a unified framework for studying whether foundation-model agents can sustain effective reasoning and action over extended task horizons. https://winniechen2002.github.io/Agent-bench-website/

• The paper introduces a comprehensive benchmark, Embodied Playground, evaluating foundation-model agents' long-horizon tasks that involve physical, spatial, and strategic reasoning.
• The benchmark utilizes 410 task-embodiment environments and subdivides them into seven families, including Launchers, Tower of Hanoi, and ManiSkill tasks, to measure an agent’s performance across various strategies and physical manipulations.
• Measurement standards include strict environmental constraints across four access levels; also analyzed was the success rate of GPT-6-Astra and Opus-5.5-extra, both within these environments. Both tested systems largely struggled to meet the performance standards.

Benchmark motivation and scope

“Embodied Playground: A Long-Horizon Agentic Robotics Benchmark for Physical, Strategic, and Spatial Reasoning” introduces an evaluation framework for foundation-model agents that must coordinate perception, planning, code generation, tool use, and physical execution over extended episodes (2610.27039). Its central claim is that conventional manipulation benchmarks do not adequately measure whether an agent can maintain a coherent task state when actions have irreversible consequences, intermediate constraints, resource costs, and delayed physical effects.

The benchmark treats robotic performance as an episode-level problem rather than as isolated policy execution. An agent receives a task specification, observations, and a robot-control interface; it then writes and executes programs through a public SDK. Subsequent observations expose the consequences of its actions, allowing it to revise its plan. Success requires more than reaching a nominal terminal configuration: the trajectory must also satisfy task-specific rules, physical validity conditions, release requirements, stability windows, and resource limits.

This formulation combines three forms of reasoning. Spatial reasoning concerns object pose, orientation, geometry, workspace relations, occlusion, and feasible approach directions. Physical reasoning concerns contact, friction, mass, momentum, fracture, stability, and delayed effects. Strategic reasoning concerns subgoal ordering, legal intermediate states, finite resources, and adaptation after failure. The paper’s main methodological contribution is to evaluate these capabilities jointly while retaining enough task structure to analyze them separately.

The interaction model is summarized below.

Figure 1: Agent–environment interaction through the public SDK, with cumulative observation and tool-access levels and execution feedback used for plan revision.

Task collection and benchmark organization

The full collection contains 410 task–embodiment environments implemented across MuJoCo, Isaac Lab, and ManiSkill. The environments cover seven broad families: launcher manipulation, Tower of Hanoi, pocket cube, Rubik’s cube, Kongming lock, bookshelf organization, and ManiSkill household, geometric, and dynamic tasks.

Family	Environments	Principal demands
Launcher	100	Aiming, indirect causality, fracture, target-order constraints
Tower of Hanoi	120	Legal planning, grasping, insertion, physical variation
Pocket cube	20	Layer turns, regrasping, state tracking
Rubik’s cube	60	State inference, face turns, fixture-mediated manipulation
Kongming lock	60	Contact-rich assembly and disassembly
Bookshelf	20	Sorting, orientation, packing, clutter correction
ManiSkill tasks	30	Household sequences, precision, dynamics

The task families deliberately couple symbolic or discrete structure with continuous control. Hanoi instances require legal ring rearrangements while preserving ordering constraints on every peg. The shortest legal plans range from 1 to 31 moves, and the physical conditions vary in support height, clearance, friction, mass, handle orientation, and obstacles. The benchmark therefore tests whether a symbolic plan remains executable under embodiment-specific geometry and perturbations.

Launcher tasks introduce indirect causal chains and finite resources. An agent may need to select a projectile trajectory that triggers rolling bodies, dominoes, structural collapse, or glass fracture, while respecting protected objects or prescribed target order. The relevant difficulty is not simply ballistic aiming; it is predicting how an intervention changes the later scene and ammunition state.

The cube and lock families are particularly demanding because they require persistent state tracking under physical interaction. Pocket-cube tasks prescribe layer transitions and require aligned detents, stable layers, and a specified number of committed quarter turns. Rubik’s-cube tasks require solving a scramble through a fixture while maintaining a consistent mapping between visual cube orientation and face-turn commands. Kongming-lock tasks require insertion or extraction sequences that are both geometrically and mechanically valid. In these settings, a logically correct plan can fail because of contact, penetration, insufficient clearance, or premature release.

The household tasks broaden the benchmark beyond abstract puzzles. Bookshelf tasks impose object-specific placement and orientation constraints, while ManiSkill tasks include preparing a writing area, packing a repair case, inserting keyed connectors, catching moving objects, closing spring-loaded doors, and transporting objects without spillage. This diversity is useful for testing transfer across robot embodiments and task semantics, although it also means that aggregate scores conflate several distinct sources of difficulty.

Agent–environment interface

All environments expose four core operations: describe, observe, step, and finish. describe provides task and interface specifications; observe returns authorized sensory information; step applies an action and advances the simulation; and finish terminates the episode for evaluation. The action space remains embodiment-specific, including joint targets, gripper commands, finger controls, and floating-hand actions.

A key design choice is the separation of observation and tool access from task difficulty. The protocol defines four cumulative access levels:

• L0: RGB images, proprioception, camera calibration, public task metadata, and self-written algorithms using standard Python, NumPy, and Pillow.
• L1: Explicitly granted perception, geometry, inverse-kinematics, motion-planning, symbolic-solving, and dynamics-proxy tools.
• L2: Pretrained grasp and contact proposal models.
• L3: Privileged read-only simulator observations, such as object poses, velocities, geometry, or contacts.

The agent still uses the same action interface at L3; privileged observations do not directly execute actions or relax task rules. This separation is important because it permits comparisons between model reasoning, perception assistance, contact modeling, and state access. However, the paper does not report controlled access-level ablations in the presented experiments, so the relative contribution of these factors remains unresolved.

The evaluation host independently determines success from the executed trajectory. The final goal must be satisfied, but so must all intermediate task rules and physical predicates. For example, Hanoi requires legal moves, correct final arrangement, valid ring seating, and one second of stability. Rubik’s-cube tasks require a solved configuration, legal fixture use, return and release, and a fresh stable interval. Launcher tasks may require target order, protected-target preservation, fracture or toppling paths, and post-impact observation. Calling finish does not provide additional settling time.

This independent evaluation prevents an agent from declaring success based only on its internal state estimate. It also exposes a distinction often omitted in embodied-agent benchmarks: a correct high-level intention does not constitute task success if the physical trajectory violates contact, stability, or mechanism constraints.

EP-100, difficulty splits, and metrics

The paper selects EP-100 as a representative 100-task subset of the 410-environment collection. It contains five equally weighted suites—Launch, Tower, Lock, Cube, and Home—and 16 task types. EP-Easy and EP-Hard divide the benchmark into disjoint 50-task splits by workload scores computed from family-specific difficulty variables. EP-20 provides a compact 20-task evaluation spanning every task type and embodiment represented in EP-100.

The workload-based split is fixed independently of evaluation outcomes. This avoids redefining difficulty as models improve, but the construction remains dependent on hand-designed family-specific scores. For example, Hanoi workload is based largely on move depth and embodiment, cube workload on sequence length and embodiment, and lock workload on subtask extent, pose, assembly direction, and dexterity. Such scores are useful for stratification but should not be interpreted as a calibrated measurement of cognitive or control difficulty.

The main benchmark metrics average success hierarchically: conditions within tasks, tasks within suites, and suites with equal weight. This prevents Launch, which contains 34 EP-100 tasks, from dominating the smaller Cube suite with 12 tasks. The protocol distinguishes ordinary success rates from an all-four-success indicator, which measures consistency across the four prescribed conditions. It also records wall-clock time for successful trials and token usage when available.

The experimental extension reports Pass@1 and Pass@2. Pass@1 uses the first valid attempt for each task-condition pair; Pass@2 measures success within the first two valid attempts. The second attempt is not treated as another reset of the same physical episode: each attempt begins from a fresh environment and agent session, while the condition itself remains fixed.

EP-20 is explicitly diagnostic rather than statistically representative. Its selection is partly informed by pilot success evidence, and its suite composition differs from EP-100, particularly for Tower and Home. Consequently, it is appropriate for rapid progress tracking but not for estimating full-benchmark performance.

Experimental results

The paper evaluates GPT-6-Astra and Opus-5.5-extra on all 100 EP-100 tasks under four conditions, producing 400 task–condition pairs. Both systems generate and execute control programs through the public SDK without auxiliary tool grants, reference trajectories, or pretrained action controllers. GPT-6-Astra uses L3 privileged observations, whereas Opus-5.5-extra is reported with extra reasoning effort; this is a substantive configuration difference that complicates a direct model comparison.

Model	EP-100 Pass@1	EP-100 Pass@2	EP-Easy Pass@1	EP-Hard Pass@1
GPT-6-Astra	13.80%	19.94%	18.42%	8.81%
Opus-5.5-extra	29.22%	35.85%	33.99%	24.21%

GPT-6-Astra solves 64 of 400 conditions on the first valid attempt and gains 27 additional successes on the second attempt. Its suite-balanced score rises from 13.80% to 19.94%, a gain of 6.15 percentage points. At the pooled condition level, the increase is from 16.00% to 22.75%. The second opportunity therefore improves coverage, but 309 conditions remain unsuccessful after two valid attempts, indicating that retry-based recovery is insufficient to overcome the dominant planning and execution failures.

Opus-5.5-extra obtains substantially higher scores: 29.22% Pass@1 and 35.85% Pass@2. The absolute difference from GPT-6-Astra is 15.42 percentage points for Pass@1 and 15.91 percentage points for Pass@2. The result is notable, but the comparison cannot isolate model capability because the systems differ in access configuration, reasoning settings, and valid-attempt counts. Moreover, the benchmark does not present a matched ablation under identical access levels and model-side budgets.

The Easy–Hard separation is meaningful for GPT-6-Astra. Its Pass@1 score decreases from 18.42% on EP-Easy to 8.81% on EP-Hard, while Pass@2 decreases from 27.41% to 12.01%. The second-attempt gain is 8.99 percentage points on EP-Easy but only 3.20 points on EP-Hard. This implies that additional attempts are more useful when the agent is already near the execution threshold; difficult tasks appear to fail at a deeper planning, state-tracking, or physical-control bottleneck.

The relationship is not uniform across suites. GPT-6-Astra’s Home Pass@1 is higher on EP-Hard than on EP-Easy, showing that the workload score does not produce monotonic difficulty for every family. The authors appropriately caution that the split is an aggregate stratification rather than a controlled psychometric scale.

Suite-level and task-level behavior

GPT-6-Astra performs best on Tower, which reaches 29.76% Pass@1 and 41.67% Pass@2. Its Tower Physics and Plan types obtain Pass@2 scores of 68.75% and 50.00%, respectively, whereas Tower Dex reaches only 9.38%. This pattern suggests that symbolic planning combined with relatively conventional parallel-gripper manipulation is substantially easier than dexterous execution. It does not, however, establish whether the limiting factor is dexterity, embodiment, observation quality, controller design, or task selection.

Launch reaches 19.12% Pass@1 and 25.74% Pass@2. Aim tasks achieve 46.43% Pass@2, but Rules tasks reach only 5.56%. The gap supports the benchmark’s distinction between direct geometric control and strategic execution under ordering and protection constraints. A system may be capable of accurate individual shots while failing to preserve the global task invariant.

Lock is substantially harder for GPT-6-Astra, with 6.25% Pass@1 and 14.06% Pass@2. All nine successful conditions in the reported Lock results occur in EP-Easy, and Lock-Dex records no successful condition. Cube is harder still: it records one successful Pocket-cube condition and no Rubik’s-cube successes, yielding 2.08% for both Pass@1 and Pass@2. The absence of Rubik successes is a strong result, but it should be interpreted as evidence of failure under this specific fixture, embodiment, and control implementation rather than as a general conclusion about cube-solving competence.

Home reaches 11.76% Pass@1 and 16.18% Pass@2. Successes are concentrated in Precise and Dynamic tasks; Tidy and Long record none. This is a notable contradiction to the assumption that familiar household semantics necessarily make tasks easier. The household tasks impose persistent object-state, preservation, ordering, and final-scene constraints that can be more demanding than isolated manipulation primitives.

Across the 100 tasks, GPT-6-Astra succeeds on at least one condition for 31 tasks with Pass@1 and 40 tasks with Pass@2. It succeeds on all four conditions for only four and nine tasks, respectively. The difference between occasional success and condition-level consistency is therefore substantial. This supports the paper’s emphasis on repeated perturbation conditions rather than single-rollout success.

Opus-5.5-extra exhibits a similar task hierarchy but at higher levels. Its Tower suite reaches 60.59% Pass@1 and 66.14% Pass@2, while Lock reaches 39.81% and 45.63%. Cube remains difficult at 6.04% and 10.72%, and Home reaches 24.17% and 29.06%. Its Launch Pass@1 of 15.51% is lower than GPT-6-Astra’s 19.12%, although its Launch Pass@2 of 27.70% is higher. This reversal indicates that aggregate model rankings can depend on attempt budget and suite-specific recovery behavior.

Failure analysis and validity of attribution

The paper’s audit classifies 645 scored GPT-6-Astra failures. The most frequent native diagnostic labels are elevation or other limit violations in Launch, penetration and displacement in Tower, contact violations in Lock, mechanism constraints in Cube, and time, penetration, or object-loss failures in Home.

Suite	Frequent failure labels
Launch	Limit: 129; rule: 58
Tower	Penetration: 35; displacement/loss: 33; limit: 27
Lock	Contact: 97; penetration: 19
Cube	Mechanism: 64; limit: 16
Home	Time: 40; penetration: 27; displacement/loss: 23

These labels are useful for trajectory-level diagnosis, but they do not directly identify causal responsibility. A contact violation may result from poor perception, an inadequate motion plan, a controller’s tracking error, or simulator-specific contact behavior. The paper correctly distinguishes native termination labels from explanations of the underlying failure.

The most consequential audit finding is that 574 of 645 scored failures, or 88.99%, were affected by incomplete rule or threshold disclosure. Examples include launcher joint-speed margins, forbidden-contact thresholds in Lock, and fixed-joint integrity tolerances in Cube. The scores remain unchanged under the audit policy, so the reported benchmark results measure completion under the implemented native contracts. However, the failure analysis cannot be interpreted solely as a measure of agent reasoning or control deficiency.

This issue substantially qualifies the empirical conclusions. The benchmark’s episode-level evaluator is technically more rigorous than a self-reported success criterion, but rigorous evaluation requires that the agent-visible contract expose all requirements relevant to success. Otherwise, the benchmark measures both embodied competence and performance under undisclosed evaluator conditions. The paper’s claim that the results demonstrate limited agent capability is therefore defensible only in the narrower sense of task completion under the native implementation; it is not fully justified as a causal diagnosis of model limitations.

The evaluation also reports 69 confirmed infrastructure incidents for GPT-6-Astra, excluded from the opportunity budget, and 41 extra attempts outside the scoring window. All 400 conditions have complete outcomes, but the audit explicitly states that it does not certify complete clearing of host or service caches or perfect simulator-state independence. These details do not invalidate the results, but they limit the strength of reproducibility claims for a benchmark whose central purpose is controlled episode-level comparison.

Limitations and open questions

The primary limitation is attribution. The experiments do not provide controlled ablations over L0–L3 access, perception tools, contact models, robot controllers, feedback frequency, or model-side token and image budgets. GPT-6-Astra receives L3 privileged observations while Opus-5.5-extra does not receive the same explicitly stated access configuration, making the headline comparison confounded.

The second limitation is contract disclosure. The 88.99% affected-failure figure is unusually large and directly bears on the interpretation of the failure analysis. Future benchmark releases need a formally complete, machine-readable task contract containing every threshold, forbidden interaction, stability criterion, and termination condition visible to the evaluated agent.

Third, the benchmark is entirely simulated in the reported experiments. The use of multiple simulators and embodiments improves diversity, but it does not establish sim-to-real validity. Physical parameters such as friction, contact compliance, joint limits, and mechanism tolerances may not transfer to hardware without calibration. The benchmark also mixes task families with different simulators and controller abstractions, which complicates cross-suite comparisons.

Finally, workload scores are heuristic and not validated against independent human, controller, or model difficulty estimates. The anomalous Home split result demonstrates that “Easy” and “Hard” should be interpreted as within-type workload partitions, not globally ordered difficulty levels. The paper leaves open whether the benchmark’s suite-balanced score correlates with real-world long-horizon reliability and whether its failure categories can be made sufficiently causal to distinguish planning errors from control and simulator errors.

Conclusion

Embodied Playground defines a broad episode-level benchmark for foundation-model agents that must maintain state, plan over dependent stages, execute through heterogeneous robot interfaces, and satisfy physical validity constraints. Its 410 environments and EP-100 subset provide substantial coverage of strategic, spatial, and physical reasoning, while the access-level protocol and independent trajectory evaluator establish a useful basis for reproducible agent comparisons.

The reported results show low and uneven performance: GPT-6-Astra achieves 13.80% Pass@1 and 19.94% Pass@2 on EP-100, while Opus-5.5-extra achieves 29.22% and 35.85%. Strong performance on selected Tower and Launch subtypes coexists with near-total failure on Rubik’s-cube and dexterous lock tasks. The benchmark therefore exposes large differences between nominal reasoning ability, occasional task completion, and robust physical execution. At the same time, the disclosure audit—affecting 88.99% of GPT-6-Astra failures—requires caution in attributing these outcomes to agent competence. The most important open question is whether the observed performance hierarchy persists under fully disclosed contracts and controlled access, controller, and sim-to-real ablations.