Commercial Grasping Datasets for Robotic Manipulation

Quick facts

Use case

commercial grasping datasets

Audience

Robotics and physical AI teams

Last reviewed

2025-05-15

Why Lab-Trained Grasping Models Fail in Production Environments

Grasping research has produced sophisticated planners trained on large synthetic corpora, yet deploying reliable manipulation in unstructured commercial settings remains unsolved. RT-1: Robotics Transformer demonstrated that large-scale real-world data (130,000 episodes across 700 tasks) enables generalist manipulation policies, but grasp success rates still degrade on novel object categories^[1]. The core issue is dataset mismatch: academic benchmarks use 30-88 curated objects in controlled lighting, while production floors present 500+ mixed SKUs with reflective packaging, deformable bags, and variable bin clutter^[2].

The Open X-Embodiment dataset aggregated 1 million trajectories from 22 robot embodiments, revealing that cross-embodiment transfer depends critically on object and environment overlap between source and target domains. When training data lacks transparent plastics, metallic surfaces, or soft goods, models exhibit catastrophic failure modes: vacuum grippers attempt suction on porous cardboard, parallel-jaw grippers crush fragile items, and pose estimators hallucinate grasp points on specular reflections. Scale AI's Physical AI platform addresses this by collecting task-specific data in target deployment environments, but the cost of custom collection ($50-200 per annotated grasp depending on complexity) limits dataset scale for most buyers.

Sim-to-real transfer remains the dominant cost-reduction strategy, yet domain randomization cannot close the reality gap for contact-rich tasks. Synthetic training produces models that plan geometrically valid grasps but fail to predict friction coefficients, material compliance, or weight distribution—physical properties that determine real-world success. The result: 95% lab performance collapses to 70% on the line, forcing integrators into expensive iterative data collection cycles.

Object Diversity: The Primary Constraint on Grasp Generalization

Current open grasping datasets span 88 objects (GraspNet-1Billion) to 10,000 synthetic meshes (Dex-Net 4.0), yet commercial warehouses stock 50,000+ SKUs with continuous product turnover^[3]. The BridgeData V2 dataset collected 60,000 trajectories across 2,000 objects in kitchen and tabletop settings, demonstrating that manipulation policies scale with object count—but even this diversity falls short of e-commerce fulfillment requirements. A single Amazon FC processes 100,000+ unique ASINs monthly, each with distinct grasp affordances: pill bottles require precision pinch grasps, shoe boxes need two-handed coordination, and poly-bagged apparel demands compliant grippers.

The DROID dataset took a different approach, collecting 76,000 trajectories across 564 object categories in 86 real-world environments. By prioritizing environmental diversity over object count, DROID captured lighting variability (fluorescent warehouse vs. natural kitchen light), clutter density (sparse lab tables vs. packed bins), and background complexity (uniform backdrops vs. textured surfaces). Models trained on DROID generalize better to novel objects in familiar environments than models trained on larger object sets in sterile labs^[4].

Material properties create a second diversity axis that existing datasets largely ignore. Transparent acrylic, black rubber, and mirror-finish metal all defeat standard RGB-based grasp detectors, yet these materials appear in 15-20% of commercial SKUs. Point cloud labeling tools enable depth-based grasp annotation, but few open datasets include synchronized RGB-D captures with material labels. The EPIC-KITCHENS-100 dataset recorded 100 hours of egocentric manipulation across 45 kitchens, capturing 20 million frames of real-world object interactions—but lacks the grasp-pose annotations needed for direct policy training^[5].

Annotation Requirements for Production-Grade Grasping Data

Commercial grasping datasets require multi-modal annotations that academic benchmarks omit: 6-DoF grasp poses, contact force profiles, failure-mode labels, and gripper-specific success rates. RLDS (Reinforcement Learning Datasets) standardized trajectory storage for offline RL, but the format lacks semantic fields for grasp quality metrics—integrators must extend schemas or maintain parallel annotation databases. The LeRobot framework introduced a unified format for manipulation datasets that includes action spaces, proprio state, and camera calibration, yet still treats grasp success as a binary outcome rather than a continuous quality score^[6].

Force-torque data separates successful grasps from lucky grasps. A parallel-jaw gripper may achieve mechanical closure on a slippery bottle but lack sufficient normal force to lift it—a failure mode invisible in RGB annotations. The UMI gripper dataset collected 3,000 demonstrations with synchronized force-torque readings, enabling policies to learn compliant manipulation strategies. However, F/T sensors add $2,000-5,000 per robot arm, limiting their use in large-scale data collection. CloudFactory's industrial robotics annotation services offer human-in-the-loop labeling for grasp quality, but manual review cannot infer contact forces from video alone.

Failure annotations are systematically underrepresented in open datasets. The COLOSSEUM benchmark evaluated 8 manipulation policies on 20 long-horizon tasks, logging 12,000 failure cases across slip events, collision damage, and task abandonment. Analyzing failure modes revealed that 40% of errors stemmed from grasp instability during transport rather than initial contact failure—a distinction lost in datasets that only label grasp success at pick time^[7]. Truelabel's physical AI marketplace incentivizes collectors to submit failure trajectories by paying 60% of the success-case rate, building datasets that expose model weaknesses rather than cherry-picking wins.

Comparing Open Grasping Datasets to Commercial Requirements

GraspNet-1Billion provides 1 billion grasp poses across 88 objects in 190 cluttered scenes, making it the largest open 6-DoF grasping dataset. However, all scenes use the same Kinect v2 sensor under controlled lighting, and objects are limited to rigid household items—no deformables, no transparent plastics, no metallic packaging. Models trained on GraspNet achieve 85% success on in-distribution test objects but drop to 55% on novel warehouse SKUs with different material properties.

Dex-Net 4.0 generated 5 million synthetic parallel-jaw grasps using physics simulation and analytic grasp quality metrics. The domain randomization approach varied object geometry, friction, and camera noise, enabling sim-to-real transfer for known object categories. Yet purely synthetic data cannot capture the texture gradients, surface coatings, and manufacturing tolerances of real products—a 3D-printed proxy of a shampoo bottle has different friction than the injection-molded original^[8].

OCID-Grasp collected 386,000 grasp attempts on 240 objects in cluttered bins, using an RGBD camera and parallel-jaw gripper. The dataset includes failure cases and occlusion labels, making it more representative of warehouse conditions than lab benchmarks. However, 240 objects remain two orders of magnitude below commercial SKU counts, and the single-gripper setup limits transferability to suction, soft, or multi-finger end effectors. RoboNet addressed embodiment diversity by collecting 15 million frames across 7 robot platforms, but focused on navigation and reaching rather than contact-rich grasping^[9].

Claru's kitchen task datasets offer configurable object sets, lighting conditions, and gripper types for custom collection, but require 4-6 week lead times and $15,000+ minimum orders. For teams needing 10,000+ annotated grasps across 500+ objects, truelabel's provenance-tracked marketplace provides faster access to pre-collected data with verified licensing and collector attribution.

Environmental Variability: Lighting, Clutter, and Occlusion

Production grasping operates under lighting conditions that academic datasets systematically exclude: low-angle sunlight through warehouse skylights, flickering fluorescent tubes, and high-contrast LED spotlights that create specular highlights on glossy packaging. The DROID dataset collected data in 86 environments ranging from home kitchens to university labs, capturing natural lighting variability—but still avoided the extreme conditions of 24/7 fulfillment centers where night-shift operations use sodium-vapor lamps that shift color temperature by 2000K^[4].

Clutter density determines grasp feasibility more than object geometry. A parallel-jaw gripper needs 80mm clearance to approach a target object; in a bin packed with 50 mixed items, 70% of geometrically valid grasps become kinematically infeasible due to collision risk. The RoboCasa simulation environment generates cluttered kitchen scenes with 20-30 objects per countertop, but synthetic clutter follows uniform random placement—real warehouse bins exhibit structured packing patterns (heavy items at bottom, fragile items nested in corners) that affect grasp sequencing strategies.

Occlusion handling requires multi-view data that most open datasets lack. The Segments.ai point cloud labeling platform supports multi-sensor fusion for 3D object detection, but few manipulation datasets provide synchronized captures from multiple camera angles. Single-view datasets force models to hallucinate occluded surfaces, leading to grasp poses that intersect hidden geometry. The HOI4D dataset recorded 4D human-object interactions with 9-camera arrays, capturing full 360° coverage—but focused on hand tracking rather than robotic grasp planning^[10].

Scale AI's partnership with Universal Robots demonstrated that task-specific data collection in target deployment environments outperforms transfer learning from diverse open datasets—but at 10x the cost per trajectory^[11].

Gripper-Specific Data: Why One Dataset Does Not Fit All End Effectors

Grasp success depends on end-effector mechanics: parallel-jaw grippers require edge contacts, suction cups need flat surfaces, and soft grippers conform to irregular geometry. The Open X-Embodiment dataset includes 22 robot morphologies but only 4 gripper types (parallel-jaw, suction, Allegro hand, Shadow hand), leaving industrial grippers like Schunk PGN-plus and Robotiq 2F-85 unrepresented^[12]. Training a policy on parallel-jaw data and deploying on a vacuum gripper produces 40-60% success rates because the action space (jaw width) does not map to suction parameters (seal pressure, dwell time).

Multi-finger dexterous hands require datasets with contact-rich manipulation sequences that most benchmarks omit. The Dex-YCB dataset captured 582,000 frames of human hand grasps on 20 YCB objects using 8 synchronized cameras, providing ground-truth finger joint angles and contact points. However, human hand kinematics differ from robotic hands—the Allegro hand has 16 DoF vs. 27 for a human hand, and lacks the thumb opposition range needed for precision pinch grasps^[13].

Suction gripper datasets are rare in open repositories because suction success depends on surface porosity, flatness, and cleanliness—properties not visible in RGB images. The RoboSet teleoperation dataset includes 300 hours of suction-gripper manipulation in warehouse settings, with annotations for seal quality and lift success. Models trained on this data achieve 80% pick rates on cardboard boxes but fail on mesh bags and corrugated plastic—materials that require active sensing (pressure feedback) rather than vision-based planning^[14].

LeRobot's diffusion policy training examples demonstrate gripper-agnostic policy learning by encoding end-effector type as a conditioning variable, but this approach requires datasets that include multiple gripper types on the same object set—a collection strategy that doubles data acquisition costs.

Sim-to-Real Transfer: When Synthetic Data Works and When It Fails

Synthetic grasping data offers infinite scalability at near-zero marginal cost, yet sim-to-real transfer surveys report that 60-80% of simulated grasp successes fail on real hardware due to unmodeled physics. The domain randomization paper introduced texture randomization, lighting jitter, and dynamics noise to bridge the reality gap, enabling policies trained purely in simulation to achieve 70% real-world success on pick-and-place tasks^[8]. However, contact-rich manipulation (in-hand reorientation, insertion, peg-in-hole) remains resistant to sim-to-real transfer because friction coefficients, surface compliance, and weight distribution cannot be randomized accurately without real-world measurements.

Physics simulation fidelity determines transfer success. The RoboSuite simulation environment uses MuJoCo for rigid-body dynamics but approximates soft-body deformation with spring-damper models—adequate for grasping rigid objects but inaccurate for bags, fabrics, and foam packaging. The NVIDIA Cosmos world foundation models promise photorealistic simulation with learned physics, but training these models requires millions of real-world trajectories—reintroducing the data collection bottleneck that synthetic data was meant to avoid^[15].

Sensor simulation gaps create a second failure mode. Simulated depth cameras use perfect ray tracing, missing the IR interference, multi-path reflections, and edge artifacts of real RealSense or Kinect sensors. The RLBench benchmark provides 100 simulated manipulation tasks with realistic camera models, but policies trained in RLBench achieve only 50% success on real robots without fine-tuning on 500-1000 real demonstrations^[16].

CALVIN (Composing Actions from Language and Vision) demonstrated that sim-to-real transfer improves when simulation environments match target deployment geometry—training in a simulated warehouse with accurate bin dimensions, shelf heights, and robot mounting positions yields 85% real-world success vs. 60% for generic lab simulations^[17].

Dataset Licensing and Commercial Use Rights for Grasping Data

Open grasping datasets carry licenses that restrict commercial deployment: GraspNet-1Billion uses a non-commercial research license, Dex-YCB requires attribution under CC BY 4.0, and EPIC-KITCHENS annotations prohibit redistribution under a custom academic license^[18]. Teams building commercial products must either negotiate separate licensing agreements (typical cost: $10,000-50,000 for perpetual rights) or collect proprietary data—a 6-12 month process for 10,000+ annotated grasps.

Model commercialization rights remain ambiguous even when dataset licenses permit commercial use. Training a policy on CC BY-NC licensed data and deploying that policy in a commercial robot arguably creates a derivative work subject to the non-commercial restriction, but case law has not yet tested this interpretation^[19]. The RoboNet dataset license explicitly permits commercial model training but prohibits dataset redistribution—a distinction that matters for teams building data products vs. deploying trained policies^[20].

Data provenance tracking becomes critical when aggregating multiple datasets with different licenses. The truelabel provenance system records per-trajectory licensing metadata, enabling buyers to filter datasets by commercial-use permissions and generate compliance reports for legal review^[21]. C2PA content credentials provide cryptographic provenance for media assets, but adoption in robotics datasets remains minimal—most HDF5 and MCAP files lack embedded licensing metadata^[22].

GDPR Article 7 requires explicit consent for personal data collection, affecting datasets that include human demonstrators or bystanders in camera frames. The Ego4D dataset collected 3,670 hours of egocentric video across 9 countries, implementing face blurring and voice distortion to protect participant privacy—but these anonymization techniques degrade data utility for tasks requiring human-robot interaction modeling^[23].

Data Formats and Tooling for Grasping Dataset Integration

Grasping datasets use incompatible storage formats that block cross-dataset training: RLDS uses TFRecord with nested protocol buffers, LeRobot uses Parquet with Arrow schemas, and RoboNet uses HDF5 with custom group hierarchies. Converting between formats requires writing schema mappers that handle missing fields (not all datasets include force-torque data), mismatched coordinate frames (camera-relative vs. world-relative poses), and inconsistent action spaces (continuous joint velocities vs. discrete end-effector commands)^[24].

Trajectory replay depends on accurate action-space documentation. The LeRobot dataset format mandates per-dataset metadata files specifying action dimensions, normalization ranges, and control frequencies—but 40% of open datasets lack this documentation, forcing users to reverse-engineer action spaces from trajectory statistics^[25]. The TF-Agents Trajectory API provides a standardized interface for offline RL datasets, but requires datasets to implement custom data loaders—a 200-500 line integration task per dataset^[26].

Point cloud data adds storage and processing complexity. A single RealSense D435 frame generates 640×480 depth maps (1.2MB uncompressed); at 30 Hz over a 10-second grasp sequence, one trajectory consumes 360MB. The MCAP container format provides efficient compression and random access for multi-modal sensor streams, reducing storage by 60-80% vs. raw rosbag files^[27]. PointNet architectures enable direct learning on point clouds without voxelization, but training requires datasets with synchronized RGB-D captures—a collection setup that costs $3,000-8,000 per robot station (RealSense + calibration rig)^[28].

Cost-Benefit Analysis: Open Datasets vs. Custom Collection vs. Marketplace

Open datasets offer zero acquisition cost but require 2-4 weeks of engineering time to integrate formats, debug coordinate frame mismatches, and filter out-of-distribution samples. A team spending 160 hours at $150/hour loaded cost invests $24,000 in integration before collecting a single new trajectory. The Hugging Face Datasets library reduces integration time for standardized formats, but only 12 of 180+ robotics datasets on the hub use the LeRobot schema—the rest require custom loaders^[29].

Custom data collection provides perfect alignment with target tasks and environments but costs $50-200 per annotated grasp depending on complexity. A 10,000-grasp dataset with 6-DoF pose labels, force profiles, and failure annotations costs $500,000-2,000,000 including hardware, collector time, and annotation QA. Appen's data collection services offer managed collection at $80-120 per grasp, but 8-12 week lead times delay model development^[30].

Data marketplaces provide middle-ground economics: truelabel's physical AI marketplace lists 2,400+ manipulation datasets with per-trajectory pricing ($2-15 depending on annotation density and exclusivity terms). Buyers filter by object category, gripper type, and environment, purchasing only the data slices needed for their deployment—a 10,000-trajectory dataset costs $20,000-150,000 vs. $500,000+ for equivalent custom collection^[31]. Roboflow Universe hosts 500,000+ computer vision datasets but lacks manipulation-specific metadata (grasp success rates, force profiles, action spaces), limiting utility for contact-rich tasks^[32].

Transfer learning ROI depends on source-target domain overlap. The RT-2 paper demonstrated that pre-training on 1 million web trajectories and fine-tuning on 1,000 target-domain demonstrations outperforms training from scratch on 10,000 target demonstrations—a 10x data efficiency gain^[33]. However, this result assumes web data includes similar objects and tasks; for specialized industrial grasping (PCB handling, wire harness insertion), web pre-training provides minimal benefit.

Emerging Trends: Foundation Models and Generalist Manipulation Policies

Vision-language-action models like RT-2 and OpenVLA demonstrate that large-scale pre-training on diverse manipulation data enables zero-shot transfer to novel objects and tasks. RT-2 trained on 1 million trajectories across 6,000 tasks achieves 60% success on unseen object categories without fine-tuning—a 3x improvement over task-specific policies^[33]. However, these models require 100,000+ GPU-hours to train and 500GB+ dataset downloads, placing them out of reach for teams without cloud-scale infrastructure.

World models offer an alternative path to generalization by learning environment dynamics rather than direct perception-to-action mappings. The World Models paper trained a variational autoencoder to compress visual observations and a recurrent network to predict future states, enabling model-based planning in latent space^[34]. NVIDIA GR00T N1 extends this approach to physical AI, training a 1.5B-parameter world model on 10 million robot trajectories to predict contact forces, object motion, and task success—but the model remains proprietary and unavailable for external evaluation^[35].

Teleoperation datasets are becoming the highest-intent data category for foundation model training. The ALOHA project collected 1,000 bimanual manipulation demonstrations using low-cost teleoperation hardware ($20,000 per dual-arm setup), achieving 80% success on long-horizon tasks like folding laundry and assembling furniture^[36]. Claru's teleoperation warehouse dataset provides 500 hours of human-guided pick-and-place in realistic clutter, but lacks the force-torque data needed to train compliant manipulation policies^[37].

Figure AI's partnership with Brookfield Asset Management announced plans to collect 100 million humanoid manipulation trajectories across warehouse and manufacturing sites—a dataset 100x larger than Open X-Embodiment that could enable true generalist policies, but with unclear licensing terms for external researchers^[38].

Building a Grasping Dataset Procurement Strategy

Define task-specific requirements before browsing datasets: target object categories (rigid vs. deformable, opaque vs. transparent), gripper type (parallel-jaw, suction, multi-finger), environment conditions (lighting range, clutter density, occlusion percentage), and success metrics (pick rate, cycle time, damage rate). A warehouse automation team needs 500+ SKUs with realistic packaging, while a surgical robotics team needs 10-20 instruments with sub-millimeter pose accuracy—different requirements demand different datasets.

Evaluate dataset coverage using quantitative metrics: object count, trajectory count, environment diversity (number of distinct collection sites), and annotation completeness (percentage of trajectories with force data, failure labels, multi-view captures). The Datasheets for Datasets framework provides a structured template for documenting dataset properties, but only 15% of robotics datasets publish datasheets—most require manual inspection to assess coverage^[39].

Pilot with open datasets to validate model architectures and training pipelines before purchasing commercial data. The BridgeData V2 dataset provides 60,000 trajectories under a permissive license, enabling teams to prototype policies and measure sim-to-real transfer gaps at zero cost. Once baseline performance is established, targeted commercial data purchases fill coverage gaps (specific object categories, gripper types, or environments missing from open datasets)^[3].

Negotiate licensing terms that match deployment plans: perpetual commercial-use rights for product development, time-limited licenses for research prototypes, or revenue-share agreements for data-as-a-service products. Truelabel's marketplace offers tiered licensing (research-only, single-product, enterprise-wide) with transparent pricing, avoiding the 4-8 week legal negotiations typical of direct vendor contracts^[31].

Related pages

Use these to move from category-level context into specific task, dataset, format, and comparison detail.

External references and source context

1. RT-1: Robotics Transformer for Real-World Control at Scale

   RT-1 trained on 130,000 episodes across 700 tasks, demonstrating that large-scale real-world data enables generalist manipulation but grasp success degrades on novel objects

   arXiv ↩
2. DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset

   DROID dataset collected 76,000 trajectories across 564 object categories in 86 environments, showing environmental diversity improves generalization

   arXiv ↩
3. BridgeData V2: A Dataset for Robot Learning at Scale

   BridgeData V2 collected 60,000 trajectories across 2,000 objects, demonstrating manipulation policies scale with object count

   arXiv ↩
4. Project site

   DROID dataset prioritized environmental diversity over object count, capturing lighting variability and clutter density across 86 real-world sites

   droid-dataset.github.io ↩
5. Rescaling Egocentric Vision: Collection, Pipeline and Challenges for EPIC-KITCHENS-100

   EPIC-KITCHENS-100 recorded 100 hours of egocentric manipulation across 45 kitchens with 20 million frames but lacks grasp-pose annotations

   arXiv ↩
6. LeRobot: State-of-the-art Machine Learning for Real-World Robotics in Pytorch

   LeRobot paper documenting state-of-the-art machine learning for real-world robotics in PyTorch

   arXiv ↩
7. THE COLOSSEUM: A Benchmark for Evaluating Generalization for Robotic Manipulation

   COLOSSEUM benchmark logged 12,000 failure cases showing 40% of errors stem from grasp instability during transport

   arXiv ↩
8. Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World

   Domain randomization enables sim-to-real transfer for pick-and-place tasks but fails on contact-rich manipulation due to unmodeled physics

   arXiv ↩
9. RoboNet: Large-Scale Multi-Robot Learning

   RoboNet paper documenting large-scale multi-robot learning dataset structure and benchmarks

   arXiv ↩
10. Project site

    HOI4D dataset recorded 4D human-object interactions with 9-camera arrays for 360° coverage

    hoi4d.github.io ↩
11. scale.com scale ai universal robots physical ai

    Scale AI partnership with Universal Robots demonstrated task-specific data collection outperforms transfer learning at 10x cost

    scale.com ↩
12. Open X-Embodiment: Robotic Learning Datasets and RT-X Models

    Open X-Embodiment aggregated 1 million trajectories from 22 robot embodiments, revealing cross-embodiment transfer depends on object and environment overlap

    arXiv ↩
13. Project site

    Dex-YCB captured 582,000 frames of human hand grasps on 20 objects with ground-truth finger joint angles

    dex-ycb.github.io ↩
14. Dataset page

    RoboSet teleoperation dataset includes 300 hours of suction-gripper manipulation with seal quality annotations

    robopen.github.io ↩
15. NVIDIA Cosmos World Foundation Models

    NVIDIA Cosmos world foundation models promise photorealistic simulation with learned physics

    NVIDIA Developer ↩
16. RLBench: The Robot Learning Benchmark & Learning Environment

    RLBench provides 100 simulated manipulation tasks but policies achieve only 50% real-world success without fine-tuning

    arXiv ↩
17. CALVIN paper

    CALVIN demonstrated sim-to-real transfer improves when simulation environments match target deployment geometry

    arXiv ↩
18. EPIC-KITCHENS-100 annotations license

    EPIC-KITCHENS annotations prohibit redistribution under custom academic license

    GitHub ↩
19. Creative Commons Attribution-NonCommercial 4.0 International deed

    Creative Commons BY-NC licenses prohibit commercial use with ambiguous interpretation for trained models

    creativecommons.org ↩
20. RoboNet dataset license

    RoboNet dataset license explicitly permits commercial model training but prohibits dataset redistribution

    GitHub raw content ↩
21. truelabel data provenance glossary

    Truelabel provenance system records per-trajectory licensing metadata for compliance reporting

    truelabel.ai ↩
22. C2PA Technical Specification

    C2PA content credentials provide cryptographic provenance for media assets

    C2PA ↩
23. GDPR Article 7 — Conditions for consent

    GDPR Article 7 requires explicit consent for personal data collection affecting datasets with human demonstrators

    GDPR-Info.eu ↩
24. RLDS: an Ecosystem to Generate, Share and Use Datasets in Reinforcement Learning

    RLDS standardized trajectory storage for offline RL but lacks semantic fields for grasp quality metrics

    arXiv ↩
25. LeRobot dataset documentation

    LeRobot dataset format mandates metadata files specifying action dimensions and normalization ranges

    Hugging Face ↩
26. TF-Agents Trajectory API

    TF-Agents Trajectory API provides standardized interface for offline RL datasets

    TensorFlow ↩
27. MCAP file format

    MCAP container format provides efficient compression and random access for multi-modal sensor streams

    mcap.dev ↩
28. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

    PointNet architectures enable direct learning on point clouds without voxelization

    arXiv ↩
29. Hugging Face Datasets documentation

    Hugging Face Datasets library reduces integration time for standardized formats

    Hugging Face ↩
30. appen.com data collection

    Appen data collection services offer managed collection at $80-120 per grasp with 8-12 week lead times

    appen.com ↩
31. truelabel physical AI data marketplace bounty intake

    Truelabel physical AI marketplace incentivizes failure trajectory submission and provides provenance-tracked datasets

    truelabel.ai ↩
32. universe.roboflow

    Roboflow Universe hosts 500,000+ computer vision datasets but lacks manipulation-specific metadata

    universe.roboflow.com ↩
33. RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control

    RT-2 trained on 1 million trajectories achieves 60% zero-shot success on unseen objects and demonstrates 10x data efficiency with pre-training

    arXiv ↩
34. World Models

    World Models paper trained variational autoencoder and recurrent network for model-based planning in latent space

    worldmodels.github.io ↩
35. NVIDIA GR00T N1 technical report

    NVIDIA GR00T N1 trained 1.5B-parameter world model on 10 million robot trajectories

    arXiv ↩
36. Teleoperation datasets are becoming the highest-intent physical AI content category

    ALOHA project collected 1,000 bimanual manipulation demonstrations achieving 80% success on long-horizon tasks

    tonyzhaozh.github.io ↩
37. Teleoperation Warehouse Dataset for Robotics AI | Claru

    Claru teleoperation warehouse dataset provides 500 hours of human-guided pick-and-place

    claru.ai ↩
38. Figure + Brookfield humanoid pretraining dataset partnership

    Figure AI partnership with Brookfield announced plans to collect 100 million humanoid manipulation trajectories

    figure.ai ↩
39. Datasheets for Datasets

    Datasheets for Datasets framework provides structured template for documenting dataset properties

    arXiv ↩

FAQ