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LADDER: A Long-Horizon Robot Manipulation Benchmark

Petko Petkov, Giuliano Albanese, Nikolay Nikolov, Luc Van Gool, Jonathan Tremblay, Danda Pani Paudel

arXiv:2610.18053v1cs.ROcs.LG

TL;DR

LADDER addresses the difficulty of isolating long-horizon robot manipulation performance when existing benchmarks use short horizons or confound horizon with other factors. It evaluates systematic horizon scaling and orchestration across simulation and real robots, finding that low-level policies collapse as tasks lengthen while tool-based orchestration performs better. Physical execution errors and practical limitations of the evaluation remain important boundaries.

  • Problem

    Existing benchmarks provide limited ways to isolate long-horizon composition because they use short horizons or entangle horizon with scene and object variation.

    Source: §1 · p. 2

  • Method

    LADDER scales tasks from 1 to 10 subgoals within the same environment across three suites, and evaluates orchestration approaches over frozen low-level policies and alternative execution backends.

    Source: §1 · p. 2

  • Results

    Low-level policies collapse as horizon grows, while tool-based GPT-Policy and PSP-Agent reach 62.0% and 40.1% overall success, respectively, versus at most 29.6% for learned-policy systems.

    Source: §1 · p. 2, §6 · p. 9

  • Takeaways & Limitations

    Task horizon is a primary failure mode, and even accurate instruction decomposition does not remove physical execution as the main bottleneck.

    Source: §6 · p. 9

    The evaluation focuses on tabletop pick-and-place, visual state tracking remains challenging under heavy clutter, and physical experiments cover fewer scenes than simulation.

    Source: §6 · p. 9

Abstract

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General-purpose robot manipulation policies learn short-horizon skills effectively, but degrade rapidly on long-horizon tasks. Existing benchmarks cannot isolate failures because task horizon is either too short or entangled with scene and object variations. We propose LADDER, a long-horizon robot manipulation benchmark scaling from 1 to 10 subgoals within the same environment over three suites (SimplerEnv-Long, RobotArena-$\infty$-Long, and RoboLab-Long), 145 tasks, eight categories, two embodiments, and 2,024 simulated episodes per system. We evaluate two orchestration paradigms: text subgoals for learned low-level policies (VoLo) and tool selection orchestration (CaP-X, GPT-Policy, PSP-Agent, TiPToP). Given the full instruction directly, low-level policies achieve 85.4% on single-subgoal tasks, but drop to near zero on extended tasks. While orchestration remedies this, it remains bounded by physical execution errors of the low-level policy rather than high-level planning and achieves only ~25%. Tool selection orchestration with GPT-Policy on GPT-6 Astra significantly outperforms learned-policy methods, but incurs significant time and cost budget and succeeds only ~62%. Real-world experiments across two embodiments confirm the collapse of the low-level policy and the gain from orchestration. Project website: https://ladder.insait.ai

1 INTRODUCTION

LADDER targets the gap between short-horizon policy training and long-horizon task composition, which existing benchmarks do not isolate cleanly. It evaluates horizon scaling and orchestration, finding sharp low-level policy collapse as tasks lengthen and improvement with orchestration, while execution remains a bottleneck.

Motivation

  • Existing benchmarks either emphasize short horizons or entangle horizon with scene clutter and instruction phrasing, limiting isolation of long-horizon composition.The paper motivates zero-shot composition because collecting demonstrations for every multi-subgoal combination is infeasible.Source: §1 · p. 2

Benchmark

  • LADDER evaluates 145 tasks across three suites, eight task categories, two embodiments, and horizons of 1–10 subgoals within the same environment.It uses programmatic predicates to measure intermediate progress as well as full task completion.Source: §1 · p. 2

Results

  • Low-level policies fall from 85.4% success on single-subgoal tasks to 5.2%–15.8% on multi-subgoal simulation tasks, with 0.5% at 8–10 subgoals.Real-world success falls to 1.2% on WidowX and 5.6% on Franka.Source: §1 · p. 2

Orchestration

  • Orchestration raises multi-subgoal success to 20.6%–22.7% in simulation and 31.0%–58.3% in real-world experiments, but low-level execution errors remain a bottleneck.Tool-selection systems go further, although some methods spend impractical budgets.Source: §1 · p. 2

2 RELATED WORK

Prior benchmarks offer limited control over task horizon: some require in-domain fine-tuning, while others focus on single-step tasks or vary horizon alongside other complexity factors. Prior orchestration research uses varied execution backends, but the effects of design choices as horizon increases remain unclear.

Manipulation benchmarks

  • CALVIN and LIBERO-Long include multi-step tasks, but their standard learned-policy evaluations use in-domain fine-tuning, limiting zero-shot composition evaluation.Their simulated environments also have limited alignment with real-world robot appearance and dynamics.Source: §2 · p. 3
  • SimplerEnv and RobotArena-∞ match simulated scenes and robot dynamics to real data, while RoboLab uses photorealistic simulation; the first two focus mainly on single-step tasks.RoboLab and RoboVoLo vary horizon alongside factors such as scene background and out-of-distribution objects.Source: §2 · p. 3

Hierarchical orchestrators

  • Prior orchestration systems use code over motion primitives, task-and-motion planning, or learned skills, but the effects of their design choices as horizon grows remain unclear.Table 1 defines average subgoals as object placements per task and incremental tasks as extensions within the same scene.Source: §2 · p. 3, Table 1 · p. 3

3 LONG-HORIZON MANIPULATION BENCHMARK

LADDER extends three established simulation platforms into long-horizon suites that vary subgoal count while controlling scene and skill conditions. It spans eight task structures and uses explicit predicates and metrics to track completion and partial progress.

Benchmark construction

  • LADDER adds controlled 1–10-subgoal scaling, eight task categories, per-subgoal predicates, and multiple embodiments across its three suites.It includes 138 incremental tasks, and 36% of tasks have six or more subgoals.Source: §3 · p. 3

Problem setting

  • A long-horizon task requires completing several sequential subgoals from one full instruction; each subgoal is a pick-and-place action, and their number defines task horizon.An episode is one execution rollout from a randomized scene layout.Source: §3 · p. 3

Task categories

  • Figure 2 presents example scenes and natural-language instructions for diverse task structures, with overlays marking target placements and constraints.The benchmark’s categories define the spatial and logical structure of instructions.Source: Figure 2 · p. 4, §3 · p. 4

Task horizon

  • Tasks scale incrementally within a scene and category, adding subgoals while keeping object sizes fixed; 138 of 145 tasks are incremental.Instructions name target objects except in exclusion tasks, and the eight categories cover spatial and logical structures such as relays, revisitations, and ordered sequences.Source: §3 · p. 4

Scene construction

  • Scene layouts are physically stable and reachable, and longer tasks add objects and a larger container without changing object size.Scripted policies validate every layout to rule out reachability as the cause of failures.Source: §3 · p. 4

Evaluation metrics

  • Task Success Rate measures episodes completing all subgoals and constraints, while Subgoal Progress measures the share of required subgoals completed.Uncompleted subgoals also receive a failure cause; step budgets grow with horizon.Source: §3 · p. 4

4 CHARACTERIZING ORCHESTRATORS

The paper characterizes orchestration by planning, triggering, visual verification, and failure recovery, and groups systems by whether they execute through a learned policy or alternative tools. These choices determine how subgoals are generated, advanced, checked, and retried or replanned.

Design decisions

  • Orchestrators are compared across four decisions: subgoal planning, trigger mechanisms, visual verification, and failure recovery.Learned-policy systems send text subgoals to a low-level policy; tool-selection systems call motion primitives or a task-and-motion planner instead.Source: §4 · p. 5

Subgoal planning

  • Planners may produce text subgoals, Python programs, symbolic goal predicates, or a next tool call, while low-level policy is the no-orchestrator baseline.VoLo replans remaining text subgoals during an episode; other learned-policy variants generate the full list at the start.Source: §4 · p. 5

Trigger mechanisms

  • Triggers range from fixed-step and proprioceptive switching to simulator-grounded switching; tool systems may decide after each primitive or after a full program.TiPToP executes open-loop without a trigger.Source: §4 · p. 5

Verification and recovery

  • Verification may be absent, VLM-based, simulator-state-based, or geometric; recovery can advance, retry, or replan after checking a subgoal.PSP-Agent combines retries for failed subgoals with replanning after all subgoals have been attempted.Source: §4 · p. 6

Evaluated systems

  • Table 2 organizes evaluated configurations by orchestrator decisions, with low-level-policy backends at the top and alternative execution backends below.The configurations correspond to the decisions shown in Figure 3.Source: Figure 3 · p. 5, Table 2 · p. 6

5 EXPERIMENTS

The experiments test horizon scaling, failure mechanisms, and simulation-to-real transfer across four sections. Low-level performance collapses as tasks lengthen, while orchestration helps but remains constrained by execution failures; real-robot results show a similar pattern.

5 EXPERIMENTS

  • The study evaluates a π0-like policy on SimplerEnv-Long and RobotArena-∞-Long and public π0.5 on RoboLab-Long, using 95% Wilson intervals for simulation results.The experiments examine horizon scaling, failure modes, and whether simulation predicts real-world performance.Source: §5 · p. 6

5.1 TASK HORIZON SCALING

  • VoLo and the Proprioceptive verifier reduce the gap to the privileged reference from 18.6 to 4.2–4.5 percentage points, but learned-policy orchestration still faces execution limits.The Proprioceptive verifier achieves 58.4% at two subgoals and 8.3% at five, while retaining partial progress through ten.Source: §5.1 · pp. 6–7
  • 85.4% on single-subgoal tasks falls to 0.0% at five subgoals for the low-level policy, while GPT-Policy leads all systems at 62.0% overall.PSP-Agent reaches 40.1% overall, compared with 25.0% for CaP-X and 9.4% for TiPToP.Source: §5.1 · pp. 6–7, Table 3 · p. 7

5.2 FAILURE ANALYSIS

  • 80.8% mean accuracy across four offline orchestrator capabilities makes Gemini 3.7 Flash the default VLM, while state tracking is hardest for most models at 70.8%.Gemini 3.7 Flash and GPT-5.6 Sol lead plan decomposition at 93.1%.Source: §5.2 · p. 8, Table 4 · p. 9
  • 32.6–43.4% of subgoals fail when unverified orchestrators move on prematurely; verification reduces this failure mode to 3.4%, shifting learned-policy failures toward low-level execution.Reaching target objects is the main low-level bottleneck, and half of failed episodes make no subgoal progress after the first 28% of the budget.Source: §5.2 · pp. 8–9

5.3 REAL-WORLD EXPERIMENTS

  • Physical WidowX and Franka experiments over horizons of 1–5 show faster low-level-policy collapse than orchestration and preserve the methods’ rankings.Only a subset of methods was tested because physical evaluations require especially high costs and time.Source: §5.3 · p. 9, Figure 7 · p. 9

6 DISCUSSION AND CONCLUSION

LADDER identifies task horizon as a primary failure mode and finds that orchestration improves multi-subgoal success, while physical execution remains the main bottleneck. Tool-based GPT-Policy and PSP-Agent outperform learned-policy systems, with high-level VLM inference and evaluation scope limiting the findings.

6 DISCUSSION AND CONCLUSION

  • GPT-Policy and PSP-Agent reach 62.0% and 40.1% overall success, respectively, versus at most 29.6% for learned-policy systems.These are the two best systems reported.Source: §6 · p. 9
  • Low-level policies collapse as task horizon grows, while high-level orchestration roughly triples overall multi-subgoal success.The paper identifies physical execution, rather than high-level planning, as the main bottleneck.Source: §6 · p. 9
  • High-level VLM inference is slow, tabletop pick-and-place is the evaluation focus, and physical experiments cover fewer scenes than simulation.Visual state tracking also remains challenging under heavy clutter.Source: §6 · p. 9

AI USE STATEMENT

The authors used LLM-based assistants to help implement, analyze, and write the paper, while retaining responsibility for its claims and code.

AI USE STATEMENT

  • The authors used LLM-based assistants for implementation, evaluation and analysis code, results analysis, methodology review, claim checking, and drafting, editing, and formatting.They report reviewing and testing all code and verifying every number, claim, and reference.Source: AI USE STATEMENT · p. 10

ETHICS STATEMENT

The authors report no human subjects, personal data, or crowdsourcing, and identify no ethical concerns beyond general safety considerations for physical robot deployment.

ETHICS STATEMENT

  • The work involved no human subjects, personal data, or crowdsourcing; the authors cite only general safety considerations for deploying learned policies on physical robots.The authors report no other ethical concerns.Source: ETHICS STATEMENT · p. 10

A LOW-LEVEL POLICY ARCHITECTURES AND TRAINING DETAILS

The appendix documents the evaluated low-level policies and orchestration systems, along with their prompts and tool catalog. It reports that π0.5 outperforms Cosmos-3 on almost every RoboLab-Long horizon, and that orchestration improves task success for both policies.

A LOW-LEVEL POLICY ARCHITECTURES AND TRAINING DETAILS

  • On RoboLab-Long, VoLo raises task success from 11.1% to 25.6% with Cosmos-3 and from 19.6% to 29.0% with π0.5; the privileged verifier reaches 22.2% and 34.4%, respectively.Cosmos-3 completes fewer subgoals than π0.5 at almost every horizon, so the main-text orchestrators use π0.5.Source: Table 5 · p. 13, A LOW-LEVEL POLICY ARCHITECTURES AND… · p. 14

B ORCHESTRATOR CONFIGURATIONS AND EXTERNAL SYSTEMS

  • The appendix describes tool-based PSP-Agent planning from images into object-destination goals, with tool feedback guiding retries and replanning after each pass.It also documents VoLo's ordered text-subgoal plans, CaP-X's generated programs over motion primitives, TiPToP's open-loop task-and-motion planning, and GPT-Policy's one-tool-call-at-a-time agent loop.Source: B ORCHESTRATOR CONFIGURATIONS AND… · pp. 14–15

C VISION-LANGUAGE PROMPTS AND TOOL CATALOG

  • PSP-Agent prompts instruct the model to decompose tasks into object-destination goals and use images and execution reports to decide whether goals are complete.For hidden containers, the prompt treats an object as placed if it is no longer elsewhere in the scene and is not held; the verification prompt counts distinct objects at the goal location.Source: C VISION-LANGUAGE PROMPTS AND TOOL… · pp. 15–16

D BENCHMARK SUITES AND COMPLETE TASK CATALOGS

The catalogs specify 145 tasks across three suites, with examples for eight task kinds and instructions spanning up to ten subgoals. They also document how episodes are initialized and budgeted across the suites.

D BENCHMARK SUITES AND COMPLETE TASK CATALOGS

  • The catalog covers 54 SimplerEnv-Long tasks, 47 RobotArena-∞-Long tasks, and 44 RoboLab-Long tasks, with examples of all eight task kinds.Table 6 pairs each kind with a representative instruction and counts tasks of that kind across the suites.Source: D BENCHMARK SUITES AND COMPLETE TASK… · p. 18, Table 6 · p. 18
  • Episode variation differs by suite: SimplerEnv-Long uses 24 seeded layouts per task, RobotArena-∞-Long uses eight fixed offsets, and RoboLab-Long jitters object position and yaw.WidowX suites allow 100 steps per subgoal; RoboLab-Long budgets vary with subgoal count, reaching 4,950 steps at ten subgoals.Source: D BENCHMARK SUITES AND COMPLETE TASK… · p. 18
  • Examples include placing all objects, transferring objects between supports, splitting destinations, and ordered relay sequences.The catalog also includes shuttle sequences and tasks that exclude specified objects or require ordered placement.Source: D BENCHMARK SUITES AND COMPLETE TASK… · pp. 19–20
  • RobotArena-∞-Long includes ordered, revisit, and duplicate-object instructions, while RoboLab-Long includes compound route, relay, revisit, distractor, and ordered-bin tasks.For RoboLab-Long, the suffix in comp relay N and comp revisit N counts objects; those tasks contain 2N and 3N subgoals, respectively.Source: Table 8 · p. 20, D BENCHMARK SUITES AND COMPLETE TASK… · pp. 21–22, Table 9 · p. 21

E REAL-ROBOT RESULTS PER TASK CATEGORY

The real-robot results are broken down by task category for WidowX and Franka, with 12 episodes per task and system. The table caption also records differences in run timing and step budgets.

E REAL-ROBOT RESULTS PER TASK CATEGORY

  • Table 10 reports subgoal progress and task success rate by task category on WidowX and Franka, using 12 episodes per task and system.The episodes comprise three layouts with four trials each.Source: E REAL-ROBOT RESULTS PER TASK CATEGORY · p. 22, Table 10 · p. 23
  • PSP-Agent ran a month later on the same tasks with layouts restored from reference images, and its first single-destination run was excluded without a recorded reason for the repeat.The excluded run had 0/12 successes and 38.3% progress.Source: Table 10 · p. 23
  • For Franka’s single-destination task, PSP-Agent had a 1,700-step budget versus 950 steps for the low-level policy and Proprioceptive verifier.Every included PSP-Agent success placed its fourth object after step 1,200.Source: Table 10 · p. 23

F RESULTS PER SUITE AND SYSTEM

The appendix reports suite-level results and paired task-bootstrap comparisons, and explains how offline VLM capabilities are scored. It identifies the tables underlying the main-text figures and suite breakdowns.

F RESULTS PER SUITE AND SYSTEM

  • Tables 11 and 13 give success rate and subgoal progress by suite, system, and subgoal count; Table 12 splits task-category success rates by suite.These tables provide the suite-level data behind Figure 4 and the combined task-category results in Table 3.Source: F RESULTS PER SUITE AND SYSTEM · p. 23, Table 11 · p. 24, Table 12 · p. 24
  • GPT-Policy exceeds PSP-Agent by 21.8 points [18.1, 25.8] in the paired overall comparison.Differences are tested by resampling tasks within each suite 10,000 times, scoring both systems on the same resampled tasks.Source: F RESULTS PER SUITE AND SYSTEM · p. 23
  • VoLo and the Proprioceptive verifier, CaP-X and the Proprioceptive verifier, and the low-level policy and TiPToP are not separated in the paired comparisons.Their reported differences are 0.3 [−1.8, 2.6], 0.0 [−3.8, 3.6], and 1.6 [−1.3, 4.7], respectively.Source: F RESULTS PER SUITE AND SYSTEM · p. 23
  • The offline VLM benchmark scores plan decomposition on 145 scenes and verification, state tracking, and next-subgoal selection on 562 frames from 143 tasks.A plan is an exact match when its object-destination pairs and multiplicity match the task’s ground-truth subgoals, in any order.Source: F RESULTS PER SUITE AND SYSTEM · p. 23

G EFFICIENCY AND TOKEN USAGE

The appendix measures efficiency using simulated robot time, VLM calls, tokens, and hosted-model API cost rather than wall-clock time. It reports a large cost difference between GPT-Policy and the other systems.

G EFFICIENCY AND TOKEN USAGE

  • Robot-time efficiency is measured by the share of subgoals completed as the simulated time budget elapses, with successful episodes counted as complete for the remainder of the budget.At the budget endpoint, this measure equals the subgoal progress reported for Figure 4.Source: G EFFICIENCY AND TOKEN USAGE · p. 26, Table 15 · p. 26
  • Wall-clock time is not used for fair system comparisons because hardware speeds differ and VLM requests wait in queues shared with other jobs.The same π0.5 policy takes 51–56 ms per step on an RTX PRO 6000 and 130–177 ms on an A100.Source: G EFFICIENCY AND TOKEN USAGE · p. 26
  • GPT-Policy costs $6.49 per successful episode versus $0.33 for PSP-Agent, while GPT-Policy’s per-episode API cost is $4.02 versus $0.13 for VoLo.The reported API cost covers hosted VLM use only; GPT-Policy’s cost is an upper bound because provider cache-served input is unreported.Source: G EFFICIENCY AND TOKEN USAGE · p. 27
  • Offline-benchmark token counts come from provider usage reports matched to scored queries; state tracking queries each subgoal separately, and local Qwen3-VL models report no usage.At least 98.6% of each model’s queries are matched, and each model ran the benchmark once.Source: G EFFICIENCY AND TOKEN USAGE · p. 27, Table 16 · p. 27

H DIAGNOSTIC FAILURE ANALYSIS

The diagnostic analysis reconstructs how subgoals are lost and attributes losses to object outcomes or orchestrator decisions. Across suites, orchestrator decisions and subgoals never started grow most with task horizon, while benchmark-specific causes and measurement caveats qualify the comparison.

H DIAGNOSTIC FAILURE ANALYSIS

  • The analysis reconstructs episodes from logged poses, success predicates, gripper states, and orchestrator events, assigning each lost subgoal one cause.The completed count exactly reproduces reported subgoal progress for every evaluated unit with an episode log.Source: H DIAGNOSTIC FAILURE ANALYSIS · pp. 27–28
  • Across every suite, orchestrator decisions and subgoals never started grow most with task horizon.Figure 8’s lower panels combine systems by task horizon; its upper panels show each system’s causes across suite tasks.Source: H DIAGNOSTIC FAILURE ANALYSIS · p. 28, Figure 8 · p. 28
  • Losses are also attributed to the orchestrator decision that made them final, with “no plan found” and “moved on before done” the two largest decisions shown.At each subgoal switch, simulator predicates check whether the abandoned subgoal was satisfied; other decisions are grouped as “other mistake.”Source: H DIAGNOSTIC FAILURE ANALYSIS · pp. 28–29
  • On RobotArena-∞-Long, GPT-Policy loses 16.6% of subgoals versus at least 66.3% for most systems; TiPToP finds no plan for 66.6% on SimplerEnv-Long.The low-level policy misses grasps on 10.2% of RobotArena subgoals, while two verifiers never issue 38.9% after retrying earlier subgoals.Source: H DIAGNOSTIC FAILURE ANALYSIS · p. 29
  • On RoboLab-Long, 90.8% of misplaced objects land in wrong containers under the low-level policy and orchestrators, versus 13.3% for PSP-Agent.The Franka almost never misses grasps; orchestrated configurations also undo more completed subgoals, and the policy picks up 61.8% of those again versus 10.5% under the low-level policy alone.Source: H DIAGNOSTIC FAILURE ANALYSIS · p. 29
  • Placement labels rely on simulator oriented bounding-box predicates, which agree with logged axis-aligned geometry for 73.0% of 504 sampled subgoals.Disagreements occur in both directions, so some “slightly off target” labels may reflect geometry error rather than robot placement.Source: H DIAGNOSTIC FAILURE ANALYSIS · p. 30
  • The online verifier achieves 88.6% precision and 61.8% recall across 23,145 calls; missed completions leave 4.4% of RobotArena-∞-Long verifier subgoals never issued.A missed completion makes the orchestrator keep commanding an already completed subgoal, consuming budget.Source: H DIAGNOSTIC FAILURE ANALYSIS · p. 30
  • Subgoal completion misses ordering or exclusion failures: these account for 2.5% of failed episodes overall and 32.3% of failed ordered-task episodes.These episodes complete every subgoal but violate the task’s ordering or exclusion rule.Source: H DIAGNOSTIC FAILURE ANALYSIS · p. 30

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