DexVerse: Modular Benchmark for Dexterous Manipulation

• DexVerse is a simulation benchmark for evaluating general-purpose hand-arm manipulation across 100 diverse, modular tasks and multiple robot embodiments.
• It features extensive multi-modal observations—including proprioception, RGB, depth, and point clouds—with configurable visual and non-visual variations to test precision and generalization.
• The benchmark supports standardized evaluation with explicit task definitions, state-based success predicates, and over 3,000 demonstration trajectories for reproducibility.

to=arxiv_search.search 天天中彩票中奖 иахьকাল_start="2024-01-01" query="DexVerse modular benchmark multi-task multi-embodiment dexterous manipulation" max_results=5 to=arxiv_search.search 天天中彩票中奖了om? to=arxiv_search.search 手机上天天中彩票json {"query":"DexVerse modular benchmark multi-task multi-embodiment dexterous manipulation","max_results":5,"start_date":"2024-01-01"} to=arxiv_search DexVerse is a large-scale simulation benchmark for dexterous manipulation designed to evaluate general-purpose hand-arm manipulation rather than isolated narrow skills. It is introduced as a modular testbed spanning task diversity, robot embodiment diversity, sensory modality diversity, and visual/generalization variability, with 100 tasks, support for 3 robot arms and 6 dexterous hands, configurable visual variation, a VR-based teleoperation pipeline, and 3,180 demonstrations with synchronized proprioceptive, RGB, depth, point-cloud, and state observations (Yao et al., 9 Jul 2026).

1. Scope, motivation, and formal task structure

DexVerse is motivated by the claim that existing benchmarks cover only subsets of the dexterous manipulation design space. Prior suites are described as tending to focus mainly on gripper-based manipulation, to provide only a small number of dexterous tasks, to emphasize only one regime such as bimanual RL or articulated objects, to lack expert demonstrations, to lack controllable visual variation, or to omit unified support for multiple arm-hand embodiments (Yao et al., 9 Jul 2026). DexVerse is intended to consolidate these missing dimensions in one benchmark.

The benchmark formalizes each task as

$$\mathcal{T}=(\Omega,\mathcal{S}_0,\mathcal{O},\mathcal{A},\mathcal{G}),$$

where $\Omega$ denotes the interactive objects in the scene, $\mathcal{S}_0$ the initial-state distribution, $\mathcal{O}$ the observation interface, $\mathcal{A}$ the action interface, and $\mathcal{G}$ the task-level success condition (Yao et al., 9 Jul 2026). This formulation makes DexVerse explicitly configuration-driven: objects, initialization, sensing, action channels, and success predicates are benchmark objects rather than implicit environment details.

A further structural feature is the modular task organization. Tasks in the same family share reusable logic for asset loading, reset/state initialization, success checking, and randomization, while variants override manipulated objects, target states, sampling ranges, and thresholds (Yao et al., 9 Jul 2026). This suggests a benchmark architecture aimed at extensibility rather than a fixed closed suite.

2. Task taxonomy and success semantics

DexVerse contains 100 tasks organized into 8 categories, with the taxonomy based on dominant interaction pattern and manipulation challenge rather than object class alone (Yao et al., 9 Jul 2026).

Category	Tasks	Examples
Primitive	9	PickCube, StackCube, RelocateSphere, PushButton
Functional	11	HammerStrike, RetrieveCup, GraspKettle, PourCan
Articulation	18	OpenStapler, OpenLaptop, SqueezeScissors, OpenPhone
Non-prehensile	5	PushT, TakeBook, PivotCuboid, PushSphereObstacle
Contact-rich	8	InsertPeg, PlugCharger, NutThread, InsertGear
Bimanual Coordination	5	BiLiftTray, BiHandover, BiLiftBox, BiLiftCart
Multi-goal	39	GraspMug + PushButton, GraspCan + TurnOnSwitch
Long-horizon	5	MakeCoffee, MicrowaveFood, CleanTable, OvenBake

This task taxonomy is one of the benchmark’s central claims of breadth. Primitive tasks emphasize direct interaction with simple goals, while articulation and contact-rich tasks target constrained motion and tight tolerances. Multi-goal tasks require simultaneous satisfaction of compositional objectives, and long-horizon tasks are staged procedural sequences rather than single terminal states (Yao et al., 9 Jul 2026).

DexVerse emphasizes explicit simulator-based success predicates. Examples include PickCube requiring the cube to be lifted at least $0.20$ m above reset height, StackCube requiring stacking within $0.035$ m horizontal and $0.025$ m vertical tolerance, PushButton requiring at least $80\%$ of button travel, OpenFaucet requiring at least $\Omega$0 of handle rotation, PlugCharger requiring less than $\Omega$1 m lateral and vertical misalignment, NutThread requiring centering within about $\Omega$2 m of the bolt axis and threading to within roughly $\Omega$3 m of target depth, and PushT requiring at least $\Omega$4 overlap with the goal footprint (Yao et al., 9 Jul 2026). These thresholds make the evaluation strongly state-based and leave little ambiguity about task completion.

The multi-goal family is generated compositionally. DexVerse pairs one of 9 primary articulated-object tasks with one of 4 rigid-object addon tasks to obtain $\Omega$5 variants, then adds 3 more where PourMug is treated as primary, producing 39 total. Success requires both the addon lift threshold $\Omega$6 and the primary articulated-task threshold $\Omega$7 to hold simultaneously (Yao et al., 9 Jul 2026). Long-horizon tasks likewise use staged success conditions; for example, MakeCoffee requires pouring milk into the mug, placing the mug under the coffee machine, and activating the machine switch in order.

3. Embodiments, action interfaces, and multimodal observations

DexVerse supports 3 robot arms—Franka Research 3, UR10e, and xArm 7—and 6 dexterous hands—Sharpa Wave, WUJI Hand, Shadow Hand, Inspire Hand, Allegro Hand, and LEAP Hand (Yao et al., 9 Jul 2026). Each hand also has a floating variant in which the wrist is directly controlled by prismatic and revolute joints. The benchmark therefore spans distinct kinematics, hand morphologies, joint limits, and actuation ranges.

A key design principle is that task environments are decoupled from robot embodiments. Each embodiment is provided through a robot configuration specifying arm and hand models, initial pose, action interface, controller parameters, and embodiment-specific constants (Yao et al., 9 Jul 2026). This makes cross-embodiment comparison a native benchmark axis rather than an afterthought.

The benchmark does not enforce one canonical low-level action space for all methods, but actions are mostly expressed as absolute joint targets. In the appendix baseline details, $\Omega$8 predicts 10-step chunks of absolute 28-/56-dimensional joint targets, OpenVLA predicts 8-step chunks of absolute joint targets, and Diffusion Policy and DP3 use action chunks of length $\Omega$9 (Yao et al., 9 Jul 2026). The 28-/56-dimensional figures correspond to single-arm/single-hand and bimanual settings.

DexVerse defines nine observation groups: policy, proprio, contact, state, privileged, goal, rgb, depth, and pointcloud (Yao et al., 9 Jul 2026). This supports state-based learning, image-based visuomotor learning, depth-based learning, point-cloud-based 3D learning, and contact-aware or asymmetric-critic regimes. The released demonstration set includes synchronized proprioception, RGB, depth, point cloud, and simulator state.

Observation presets standardize common sensing configurations. The appendix lists state, rgb, rgb_depth, pointcloud, and multiview variants such as 3view_rgb, 3view_rgb_depth, and 3view_pointcloud; most visual presets use history length 3, while the state preset uses history length 0 (Yao et al., 9 Jul 2026). This makes matched-modality comparison straightforward.

Visual variation is a first-class benchmark axis. At reset, DexVerse can randomize object materials/textures, table materials, lighting conditions, background skyboxes, exposure, color temperature, and camera viewpoints. It uses 100 HDR skyboxes from Poly Haven and random table materials from Isaac assets, and visual and non-visual variations can be toggled independently or jointly (Yao et al., 9 Jul 2026). Non-visual perturbations include object initial poses, task sampling ranges, proprioceptive and object-state noise, and dynamics parameters. This makes DexVerse explicitly a perception-and-control benchmark rather than only a control benchmark.

4. Demonstration collection and dataset design

DexVerse collects demonstrations with an embodiment-adaptive VR teleoperation pipeline using Apple Vision Pro and Isaac Lab’s CloudXR-based XR teleoperation interface (Yao et al., 9 Jul 2026). Human wrist pose becomes the target end-effector pose, the robot arm tracks that target through inverse kinematics, and human hand motion is converted to target robot-hand joint poses using optimization-based dex-retargeting following AnyTeleop-style retargeting.

The benchmark releases 3,180 demonstration trajectories. For each of 56 single-goal tasks, it provides 55 demonstrations: 50 with the Shadow Hand and 1 each with the other five hand embodiments. For each of 5 long-horizon tasks, it provides 20 demonstrations (Yao et al., 9 Jul 2026). This distribution is intentionally broad in task coverage but not uniform across embodiments; Shadow Hand dominates the demonstration corpus.

Each demonstration is stored as a sequence of action-state pairs recorded during teleoperation, but the released dataset is replayed by restoring simulator states and regenerating requested observations locally (Yao et al., 9 Jul 2026). The paper emphasizes two consequences. First, portability and reproducibility improve because state replay avoids accumulated mismatch caused by floating-point and physics differences. Second, storage remains compact and flexible because users can regenerate observation presets or camera inputs without duplicating the entire dataset.

This dataset design is unusually aligned with benchmark reuse. A plausible implication is that DexVerse is optimized not only for end-user training but also for systematic ablation over sensing conditions, since the same teleoperated state trajectory can be re-observed under different observation presets without recollecting demonstrations.

5. Benchmarking methodology and empirical findings

Although DexVerse defines 100 tasks, the reported baseline study evaluates 19 tasks using 950 training episodes total, computed as 19 tasks $\mathcal{S}_0$0 50 episodes per task (Yao et al., 9 Jul 2026). All methods are evaluated in the same simulator under the same termination criteria, with 50 closed-loop evaluation episodes per task and mean online success rate as the primary metric.

The four reported baseline families are Diffusion Policy, DP3, OpenVLA, and $\mathcal{S}_0$1 (Yao et al., 9 Jul 2026). Diffusion Policy is used in a state-based variant with normalized proprioceptive input only. DP3 uses point clouds plus proprioception, with $\mathcal{S}_0$2 points, $\mathcal{S}_0$3, $\mathcal{S}_0$4, and DDIM training over 100 timesteps. OpenVLA is fine-tuned with a continuous $\mathcal{S}_0$5 regression head. $\mathcal{S}_0$6 is fine-tuned from an official 3.3B checkpoint using RGB images and language but no proprioceptive input.

The headline result is that the benchmark remains far from solved. The best mean online success rate is $\mathcal{S}_0$7, achieved by DP3 and $\mathcal{S}_0$8, followed by Diffusion Policy at $\mathcal{S}_0$9 and OpenVLA at $\mathcal{O}$0 (Yao et al., 9 Jul 2026). The paper also reports that no single policy dominates across all skill families. Diffusion Policy is strongest on simple Pick-and-Lift with category mean $\mathcal{O}$1; DP3 is strongest on Tool Use with category mean $\mathcal{O}$2; and $\mathcal{O}$3 is strongest on Articulated-object manipulation and Precision contact with means $\mathcal{O}$4 and $\mathcal{O}$5, respectively (Yao et al., 9 Jul 2026).

Per-task behavior exposes substantial heterogeneity. BimanualLiftCarton is solved well by several methods, including $\mathcal{O}$6 at 1.00 and DP at 0.94. GraspKettle favors diffusion baselines, with DP3 at 0.80 and DP at 0.90, while OpenVLA reaches 0.16. OpenStapler is comparatively tractable for all four baselines, including OpenVLA at 0.92. FunctionalHammerStrike remains difficult for every method, and PushSmallSphereObstacleSlope strongly favors $\mathcal{O}$7 at 0.82 over the others (Yao et al., 9 Jul 2026).

Several tasks are effectively unsolved. PushT is 0.00 for all four methods, SlideUtilityKnife is 0.00 for all, InsertPen is near-zero for all, and OpenLaptop is near-zero for all (Yao et al., 9 Jul 2026). The paper attributes this headroom to sub-centimeter alignment, force/contact regulation, sustained constrained interaction, and fine timing. It also argues that internet-scale VLA pretraining does not yet translate into a clear dexterous advantage, since $\mathcal{O}$8 only matches the best diffusion baseline on average while OpenVLA trails significantly. The stated interpretation is a mismatch between pretraining data from web imagery and low-DoF robots and the target regime of high-DoF multifinger dexterous control (Yao et al., 9 Jul 2026).

6. Relation to neighboring dexterous manipulation research

DexVerse is primarily a benchmark and dataset contribution rather than a policy method. This distinguishes it from FAR-Dex, which addresses few-shot arm-hand coordination for a 7-DoF arm plus a 10-DoF dexterous hand using IsaacLab-based demonstration expansion and adaptive residual refinement, reporting a 13.4-point data-quality gain over DemoGen on Insert Cylinder, an average 7-point simulation success-rate gain over ResiP across four dexterous tasks, and 80–90% real-world success across all tasks (Bai et al., 11 Mar 2026). FAR-Dex targets practical policy learning under severe demonstration scarcity, whereas DexVerse supplies a broader evaluation substrate.

DexVerse also differs from DexVLA, which frames the bottleneck in vision-language-action robotics as action representation and introduces a 1B-parameter diffusion action expert together with a three-stage embodied curriculum. DexVLA evaluates across single-arm, bimanual, and dexterous-hand embodiments, reports a 0.92 normalized score on shirt folding after Stage 2 only, and reports an average of 0.90 on two new-embodiment adaptation tasks using only 100 demonstrations per task (Wen et al., 9 Feb 2025). DexVLA addresses language-conditioned control and cross-embodiment learning; DexVerse provides a standardized dexterous environment in which such systems can be compared.

At the representation layer, UniDexTok addresses a separate but related cross-embodiment problem by proposing the Unified Dexterous Hand Model and a retargeting-free state tokenizer over a shared 22-DoF semantic interface. It reports MPJAE reduction from 15.63 degrees to 0.16 degrees and MPJPE reduction from 18.51 mm to 0.18 mm relative to UniHM, and further reports zero-shot and few-shot reconstruction on a new dexterous hand (Fang et al., 9 Jun 2026). This suggests a complementary infrastructure direction for DexVerse: benchmark-scale task diversity on one side, and benchmark-scale hand-state standardization on the other.

For cluttered grasping specifically, DexGraspNet 2.0 introduces a large-scale synthetic dexterous grasping benchmark with 1319 objects, 8270 scenes, and 427 million grasps, together with a local-geometry-conditioned diffusion method and a reported 90.7% real-world dexterous grasping success rate in cluttered scenes (Zhang et al., 2024). DexGraspNet 2.0 is narrower in task scope but deeper in scene-level grasp annotation. For sim-to-real transfer, DexSim2Real instead emphasizes foundation-model-guided domain randomization, visuo-tactile fusion, and curriculum learning, reporting 78.2% average real-world success over six dexterous tasks and an 8.3% average sim-to-real gap (Zeng et al., 3 May 2026). These works occupy adjacent layers of the dexterous stack that DexVerse leaves open by design.

7. Limitations, misconceptions, and future directions

A common misconception would be to treat DexVerse as a new policy-learning method. The paper is explicit that there is no benchmark-wide reward formula or central RL objective; rewards appear only as optional terms in Isaac Lab manager-based environments, while the benchmark itself is centered on task definitions, success predicates, and standardized rollout evaluation (Yao et al., 9 Jul 2026). Its main novelty is the combination of breadth, modularity, multi-embodiment support, multimodal observations, and demonstration data.

The benchmark also has important practical limitations. The current release is simulation-focused and does not yet address real-robot transfer directly. Demonstration coverage is broader than prior dexterous benchmarks but remains uneven across embodiments because Shadow Hand dominates the demonstration set. Quantitative baseline evaluation covers only 19 of the 100 tasks, so the full task inventory remains only partially benchmarked in the published results (Yao et al., 9 Jul 2026). These limitations do not negate the benchmark’s value, but they matter for interpretation.

Future work identified by the authors includes real-robot transfer, more demonstrations across additional embodiments, expanded task families, and broader standardization for cross-task and cross-embodiment evaluation (Yao et al., 9 Jul 2026). The empirical headroom already points toward where such effort is likely to matter most: precision contact, tool use, long-horizon procedures, and hard constrained interactions. More broadly, the coexistence of benchmark efforts such as DexVerse with algorithmic efforts such as FAR-Dex, DexVLA, UniDexTok, DexGraspNet 2.0, and DexSim2Real suggests a layered research program in which benchmark coverage, hand-state standardization, policy scaling, cluttered grasp generation, and sim-to-real transfer remain distinct but tightly coupled problems (Bai et al., 11 Mar 2026, Wen et al., 9 Feb 2025, Fang et al., 9 Jun 2026, Zhang et al., 2024, Zeng et al., 3 May 2026).