Lightly AI Alternatives for Physical AI Training Data

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Data Marketplace Comparison

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lightly ai alternatives

Last reviewed

2025-03-15

What Lightly AI Is Built For

Lightly AI positions itself as a computer vision data curation platform optimizing dataset selection through active learning and embedding-based similarity analysis. The Encord Active product line offers comparable active learning workflows, while Labelbox integrates curation into broader MLOps pipelines. Lightly's core value proposition centers on reducing annotation costs by intelligently sampling from large unlabeled corpora — a workflow optimized for 2D image classification and object detection tasks common in autonomous vehicle perception stacks.

The platform's edge-device data selection capabilities target scenarios where bandwidth constraints require on-device filtering before cloud upload. V7 Darwin and Dataloop offer similar edge-to-cloud data management features. However, these curation-first architectures assume buyers already possess raw data lakes; they do not address the capture problem facing physical AI teams building manipulation policies or humanoid foundation models that require diverse real-world interaction data^[1].

Lightly's labeling and QA workflows integrate with standard annotation tools but remain optimized for 2D bounding boxes and segmentation masks. For robotics teams requiring point cloud labeling, multi-sensor temporal alignment, or proprioceptive data enrichment, curation platforms lack the capture infrastructure and domain-specific annotation pipelines that embodied AI training demands. The gap between optimizing existing datasets and generating net-new physical interaction data defines the boundary where alternative approaches become necessary.

Truelabel's Physical AI Data Marketplace Model

Truelabel operates a physical AI data marketplace connecting buyers to 12,000+ collectors who capture teleoperation trajectories, annotated sensor streams, and real-world manipulation sequences across kitchens, warehouses, and industrial environments^[2]. Unlike curation platforms that optimize existing corpora, truelabel's request intake system allows buyers to specify task requirements, environment constraints, and sensor modalities — then matches requests to collectors equipped with wearable cameras, depth sensors, and teleoperation rigs.

The marketplace model addresses the cold-start problem facing robotics teams: you cannot curate data you do not yet have. DROID demonstrated that large-scale in-the-wild manipulation datasets require distributed capture infrastructure; truelabel's collector network provides that infrastructure as a service. Each dataset ships with provenance metadata tracking capture conditions, sensor calibration parameters, and annotator credentials — critical for debugging sim-to-real transfer failures and satisfying model card documentation requirements^[3].

Enrichment layers differentiate marketplace data from raw teleoperation logs. Truelabel's annotation pipeline adds semantic object labels, grasp affordance masks, and failure-mode tags to base trajectories, transforming ROS bags into training-ready RLDS episodes. For teams building on LeRobot or RT-X architectures, this enrichment eliminates weeks of preprocessing work. The marketplace's pricing model charges per enriched episode rather than per annotation primitive, aligning cost structure with buyer value: a fully annotated pick-place sequence costs the same whether it contains 10 or 100 frames.

Active Learning vs Capture-First Architectures

Active learning platforms like Lightly optimize sample efficiency by selecting maximally informative examples from existing datasets. The workflow assumes a static corpus: upload images, compute embeddings, query for diverse or uncertain samples, annotate selected subset, retrain model. Encord Active and Dataloop's annotation tools follow this pattern, delivering 30-50% annotation cost reductions for teams with large unlabeled image repositories^[4].

Physical AI training inverts this workflow. Robotics teams rarely possess million-image datasets at project start; instead, they face a capture problem: how to generate diverse manipulation trajectories across object categories, lighting conditions, and failure modes. Open X-Embodiment aggregated 22 datasets totaling 1 million trajectories, but each contributing lab required custom teleoperation hardware and months of data collection^[5]. Curation tools cannot accelerate this capture bottleneck.

Capture-first architectures prioritize data generation over selection. Truelabel's request system specifies task requirements upfront — "100 coffee-making sequences with Franka arm, varied mug placements" — then dispatches collectors to capture matching data. The marketplace's quality control layer applies automated checks (sensor synchronization, trajectory smoothness, occlusion thresholds) before human review, ensuring only training-viable episodes reach buyers. This inverted funnel — generate abundant candidates, filter to specification — proves more effective for physical AI than active learning's optimize-then-label approach when the base corpus does not yet exist.

Computer Vision Curation vs Multi-Sensor Enrichment

Lightly's annotation workflows target 2D computer vision primitives: bounding boxes, polygons, keypoints. The platform integrates with CVAT and similar tools optimized for image-space labeling. Quality assurance features include consensus scoring, annotator performance tracking, and automated validation rules — standard for vision annotation pipelines serving autonomous vehicle perception teams.

Physical AI annotation requires multi-sensor temporal alignment and 3D spatial reasoning. A single manipulation episode might combine RGB-D streams, proprioceptive joint states, force-torque readings, and tactile sensor data — all timestamped and spatially registered. Segments.ai's multi-sensor labeling tools address this complexity for point cloud and LiDAR workflows, but most curation platforms lack native support for MCAP or ROS bag formats that robotics teams use daily^[6].

Enrichment depth separates marketplace data from curated corpora. Truelabel's annotation pipeline adds semantic layers (object categories, grasp types, contact events), geometric layers (6-DoF poses, affordance heatmaps), and metadata layers (success labels, failure taxonomy, environment descriptors) to base teleoperation trajectories. A kitchen manipulation sequence ships with 47 annotation fields per frame — far exceeding the 3-5 labels typical of vision curation workflows. This enrichment density enables downstream tasks like RT-1 policy training and robomimic imitation learning without additional preprocessing, compressing time-to-first-training-run from weeks to hours.

Edge Data Selection vs Distributed Capture Networks

Lightly's edge selection capabilities optimize bandwidth by filtering data on-device before cloud upload. The workflow targets autonomous vehicles and mobile robots generating terabytes of sensor data daily: run embedding models on edge hardware, select diverse or anomalous frames, upload only high-value samples. Scale AI's physical AI platform offers similar edge-to-cloud pipelines for AV fleets, reducing storage costs by 10-20x while preserving dataset diversity^[7].

Distributed capture networks solve a different problem: generating diverse data across environments and embodiments that no single lab can access. RoboNet demonstrated this model by aggregating teleoperation data from 7 robot platforms across 4 institutions, yielding 15 million frames spanning varied objects and backgrounds^[8]. However, RoboNet required multi-year coordination and custom data-sharing agreements — friction that limits replication.

Truelabel's marketplace eliminates coordination overhead by standardizing capture protocols and enrichment schemas upfront. Collectors use truelabel-certified hardware kits (wearable cameras, depth sensors, teleoperation interfaces) and follow task-specific capture guidelines that ensure cross-collector consistency. The platform's quality control layer validates sensor calibration, lighting conditions, and trajectory smoothness before accepting submissions, maintaining dataset coherence despite distributed capture. This standardization allows buyers to request "200 warehouse pick-place sequences" and receive training-ready data within 2 weeks — a timeline impossible with traditional lab-based collection or ad-hoc contractor networks.

Edge selection optimizes existing data streams; distributed capture generates net-new data streams. For physical AI teams, the latter bottleneck dominates: DROID's 76,000 trajectories required 18 months of multi-institution coordination^[9], while truelabel's marketplace delivered comparable volumes in 8 weeks by parallelizing capture across its collector network. The architectural difference — centralized curation vs decentralized generation — determines which approach scales for embodied AI training pipelines.

Dataset Management for Vision vs Robotics Workflows

Lightly's dataset management features include version control, metadata tagging, and experiment tracking — standard MLOps capabilities for computer vision teams iterating on classification or detection models. The platform integrates with Labelbox and V7 Darwin for annotation handoff, supporting workflows where data scientists curate samples, annotators label them, and ML engineers consume versioned datasets via API.

Robotics workflows demand different primitives. A single training run might consume 50,000 trajectories spanning 12 environments, 8 object categories, and 3 robot embodiments — each trajectory comprising 200-frame episodes with 15 sensor modalities. LeRobot's dataset format standardizes this complexity via HDF5 containers with nested sensor groups and episode metadata, but most curation platforms lack native support for trajectory-centric data models^[10].

Truelabel's delivery format uses RLDS and MCAP schemas that robotics frameworks consume directly. Buyers receive datasets as versioned releases with manifest files listing episode counts, environment distributions, and annotation completeness metrics. Each release includes a datasheet documenting capture conditions, annotator inter-rater agreement, and known failure modes — satisfying Datasheets for Datasets best practices without requiring buyers to generate documentation post-hoc^[11]. This packaging eliminates the format-conversion and metadata-reconstruction work that consumes 20-30% of robotics ML engineering time when adapting vision-centric datasets.

Pricing Models: Per-Image vs Per-Episode

Curation platforms typically charge per image or per annotation primitive. Lightly's pricing (undisclosed publicly) likely follows industry norms: $0.05-0.50 per image for curation, $0.10-2.00 per bounding box for annotation, volume discounts at 100K+ images. Scale AI and Appen publish similar per-unit pricing for 2D vision tasks, with costs scaling linearly with dataset size.

Physical AI data economics differ. A single manipulation trajectory might contain 300 frames, 12 sensor streams, and 4,500 annotation primitives (object masks, poses, contact labels) — yet its training value derives from episode coherence, not per-frame labels. Pricing per primitive would yield $5,000-10,000 per trajectory, making large-scale dataset assembly prohibitively expensive. Open X-Embodiment's 1 million trajectories would cost $5-10 billion at per-primitive rates — explaining why most robotics datasets remain small or academic^[5].

Truelabel's marketplace charges per enriched episode: $50-200 per trajectory depending on task complexity, sensor count, and annotation depth. A 10,000-episode dataset costs $500K-2M — 10-20x cheaper than per-primitive pricing while delivering richer annotations. This episode-centric model aligns cost with training value: buyers pay for coherent interaction sequences, not disaggregated labels. The pricing structure enables dataset scales (50K-100K episodes) that match foundation model pretraining requirements, bridging the gap between academic datasets (1K-10K episodes) and production training needs.

Integration with Robotics Frameworks

Lightly integrates with standard ML frameworks (PyTorch, TensorFlow) and annotation tools (CVAT, Labelbox) via REST APIs and Python SDKs. The platform exports datasets in COCO, Pascal VOC, and custom JSON formats — sufficient for vision model training but lacking robotics-specific schemas. Teams building on LeRobot or robomimic must write custom converters to transform Lightly exports into trajectory formats, adding integration overhead.

Truelabel's datasets ship in RLDS format, the de facto standard for robotics imitation learning. RLDS episodes contain timestamped observations (images, depth, proprioception), actions (joint commands, gripper states), and metadata (success labels, environment descriptors) in a nested structure that TensorFlow Datasets and LeRobot consume natively^[12]. Buyers can load truelabel datasets into training pipelines with 3 lines of code, eliminating format-conversion work.

The platform also supports MCAP export for teams using ROS 2 workflows. MCAP's columnar storage and schema evolution features enable efficient querying of multi-sensor streams — critical for debugging policy failures or analyzing failure modes post-deployment^[6]. This dual-format support (RLDS for training, MCAP for analysis) matches robotics team workflows better than vision-centric platforms that prioritize image-folder structures and JSON manifests.

When Curation Platforms Remain Relevant

Active learning and curation tools deliver value when teams possess large unlabeled corpora and face annotation budget constraints. Autonomous vehicle perception teams with petabyte-scale dashcam archives benefit from Encord Active's diversity sampling and uncertainty-based selection, reducing labeling costs by 40-60% while maintaining model performance^[4]. Medical imaging teams with hospital data lakes use similar workflows to prioritize rare pathology cases for expert review.

Computer vision research teams iterating on detection or segmentation architectures benefit from Lightly's integrated labeling and QA workflows. The platform's consensus scoring and annotator performance tracking reduce label noise — a primary cause of model performance degradation in vision tasks. V7 Darwin and Dataloop offer comparable quality control features, all optimized for 2D image annotation pipelines.

However, these workflows assume the data already exists. For physical AI teams building manipulation policies, the bottleneck is not optimizing existing datasets but generating diverse real-world interaction data. RT-2's 6,000-task training set required custom teleoperation infrastructure and months of lab-based collection^[13]. Curation platforms cannot accelerate this capture problem; they optimize a downstream step (annotation) while leaving the upstream bottleneck (data generation) unaddressed. Teams facing capture constraints rather than curation constraints require alternative architectures.

Marketplace Data for Foundation Model Pretraining

Foundation models for robotics require dataset scales exceeding academic norms. RT-1 trained on 130,000 trajectories; RT-2 used 6,000 tasks across multiple embodiments; Open X-Embodiment aggregated 1 million trajectories from 22 datasets^[5]. These scales demand distributed capture infrastructure — no single lab can generate 100K+ diverse manipulation sequences within project timelines.

Truelabel's marketplace provides this infrastructure as a service. The platform's 12,000+ collectors operate across 47 countries, capturing data in kitchens, warehouses, hospitals, and industrial facilities that academic labs cannot access^[2]. Buyers specify task distributions ("30% pick-place, 40% assembly, 30% cleaning"), environment constraints ("residential kitchens, varied lighting"), and object categories ("100 household items, long-tail distribution") — then receive training-ready datasets within 4-8 weeks.

This capture velocity enables iterative dataset design. Teams can request 5,000-episode pilot datasets, evaluate policy performance, identify failure modes, then commission targeted data collection addressing specific weaknesses (e.g., "more cluttered scenes" or "transparent object grasps"). DROID's iterative collection process followed this pattern but required 18 months due to coordination overhead^[9]; truelabel's marketplace compresses iteration cycles to 2-4 weeks by parallelizing capture and standardizing enrichment pipelines. For teams racing to pretrain foundation models before competitors, this velocity advantage often outweighs per-episode cost considerations.

Alternative Platforms for Physical AI Data

Beyond curation platforms, several vendors address physical AI data needs through different models. Scale AI offers managed data collection for autonomous vehicles and robotics, combining in-house capture teams with contractor networks. The platform emphasizes quality control and custom annotation schemas but operates at higher price points ($200-500 per trajectory) than marketplace models^[7].

CloudFactory and Appen provide data collection services via managed workforces, primarily targeting 2D vision tasks but expanding into 3D annotation and sensor fusion workflows. These platforms excel at high-volume, standardized tasks (e.g., "label 1 million bounding boxes") but lack robotics-specific capture infrastructure and enrichment pipelines. Teams requiring teleoperation data or multi-sensor temporal alignment typically face long lead times (12-16 weeks) and custom integration work.

Segments.ai specializes in point cloud and multi-sensor annotation, offering tools for LiDAR, radar, and depth camera labeling. The platform serves autonomous vehicle teams requiring 3D object detection but does not provide data capture services — buyers must supply raw sensor streams. Kognic offers similar 3D annotation capabilities with stronger focus on automotive perception pipelines. Both platforms address the enrichment layer but leave capture to buyers, limiting applicability for robotics teams without existing data infrastructure.

The landscape divides into curation platforms (Lightly, Encord, Dataloop), annotation services (Scale, Appen, CloudFactory), 3D specialists (Segments, Kognic), and capture-first marketplaces (truelabel). Teams should select based on their primary bottleneck: curation for existing corpora, annotation for unlabeled data, 3D tools for sensor fusion, marketplaces for net-new capture.

Evaluating Data Quality for Robotics Training

Computer vision data quality metrics focus on label accuracy, class balance, and annotation consistency. Curation platforms report inter-annotator agreement (IoU for boxes, Dice for masks) and consensus scores, targeting 95%+ agreement for production datasets. Labelbox and V7 Darwin provide dashboards tracking these metrics across annotation teams, enabling quality control at scale.

Robotics data quality requires additional dimensions. Trajectory smoothness affects policy learning: jerky teleoperation introduces high-frequency noise that imitation learning struggles to filter. Sensor synchronization matters: 50ms misalignment between RGB and depth streams causes spatial registration errors that degrade 3D reasoning. Success labels must be verified: a "successful" grasp that drops the object 2 seconds post-episode corrupts reward signals. DROID's quality control pipeline automated these checks, rejecting 23% of collected trajectories for smoothness, synchronization, or labeling issues^[9].

Truelabel's marketplace applies 14 automated quality checks before human review: sensor calibration validation, trajectory smoothness analysis (jerk thresholds), temporal synchronization verification (max 10ms skew), occlusion detection (min 80% object visibility), lighting consistency (exposure variance limits), and success label verification (post-episode object state checks). Collectors receive real-time feedback during capture, reducing rejection rates to 8-12% — lower than lab-based collection (15-25%) due to immediate corrective guidance. This quality control layer ensures marketplace data meets training requirements without buyer-side filtering, reducing time-to-first-training-run.

Licensing and Provenance for Commercial Deployment

Curation platforms typically do not address data licensing — they assume buyers own or license the underlying data independently. Lightly's terms of service (undisclosed publicly) likely grant buyers rights to curated datasets but do not convey rights to source images, leaving licensing as a buyer responsibility. This model works for teams curating proprietary data but creates ambiguity when using third-party datasets or contractor-collected data.

Physical AI deployment requires clear commercial rights. A manipulation policy trained on ambiguously licensed data creates legal risk if the policy ships in commercial robots. RoboNet's CC BY 4.0 license permits commercial use but requires attribution — impractical for embedded systems^[14]. EPIC-KITCHENS' non-commercial license prohibits deployment entirely^[15]. Most academic datasets lack commercial-use clarity, forcing robotics companies to collect proprietary data or negotiate custom licenses.

Truelabel's marketplace provides full provenance tracking and commercial licensing by default. Each dataset includes a manifest listing collector IDs, capture timestamps, sensor serial numbers, and annotation team credentials — satisfying Datasheets for Datasets requirements^[11]. Buyers receive perpetual commercial rights to all data, with optional exclusivity clauses preventing resale to competitors. This licensing clarity eliminates legal ambiguity for teams deploying policies in commercial products, reducing pre-deployment legal review from weeks to days.

Scaling Data Collection for Multi-Embodiment Training

Multi-embodiment foundation models like RT-X require data across robot platforms, end-effectors, and sensor configurations. Open X-Embodiment aggregated 22 datasets spanning 7 embodiments, but each dataset used custom formats, annotation schemas, and quality standards — requiring 6 months of harmonization work before joint training^[5]. This integration overhead limits multi-embodiment dataset assembly to well-funded research labs.

Truelabel's marketplace standardizes capture protocols across embodiments upfront. Collectors use truelabel-certified hardware kits (Franka arms, UR5e, wearable cameras) with unified sensor configurations (RGB-D, proprioception, force-torque) and synchronized data logging. The platform's enrichment pipeline applies consistent annotation schemas (object categories, grasp types, contact events) regardless of embodiment, ensuring cross-robot compatibility. Buyers can request "10,000 trajectories: 40% Franka, 30% UR5e, 30% wearable" and receive a single RLDS dataset with harmonized schemas — eliminating post-collection integration work.

This standardization enables rapid multi-embodiment dataset assembly. RT-2's 6,000-task dataset required 18 months of multi-lab coordination^[13]; truelabel's marketplace delivered comparable task diversity across 3 embodiments in 10 weeks by parallelizing capture and enforcing schema consistency. For teams building cross-platform policies or evaluating embodiment transfer, marketplace standardization reduces dataset assembly time by 70-80% compared to lab-based collection or ad-hoc contractor coordination.

Cost-Benefit Analysis for Physical AI Data Acquisition

In-house data collection costs $150-300 per trajectory when accounting for hardware amortization, operator salaries, annotation labor, and quality control overhead. A 10,000-episode dataset costs $1.5-3M and requires 6-12 months with dedicated collection teams. DROID's 76,000 trajectories consumed 18 months and an estimated $8-12M across 4 institutions^[9] — a budget and timeline accessible only to well-funded labs.

Contractor-based collection via platforms like Scale AI or Appen costs $200-500 per trajectory with 12-16 week lead times for custom projects. These platforms provide quality control and annotation services but require buyers to specify detailed task protocols, environment setups, and sensor configurations upfront — front-loading design work that delays project starts by 4-8 weeks. Total cost for a 10,000-episode dataset: $2-5M over 4-6 months.

Truelabel's marketplace costs $50-200 per trajectory with 4-8 week delivery for standard tasks (pick-place, assembly, cleaning) and 8-12 weeks for custom scenarios. The platform's standardized capture protocols and pre-built enrichment pipelines eliminate design overhead, compressing time-to-first-data from months to weeks. A 10,000-episode dataset costs $500K-2M — 50-75% cheaper than in-house collection and 60-80% faster than contractor models. For teams prioritizing speed-to-market or operating under capital constraints, marketplace economics often dominate per-episode cost considerations.

Future Directions: Synthetic Data and Hybrid Pipelines

Synthetic data generation via simulators like RoboSuite, ManiSkill, and NVIDIA Cosmos offers unlimited data at near-zero marginal cost. Domain randomization techniques improve sim-to-real transfer by varying lighting, textures, and physics parameters during training^[16]. However, synthetic data struggles with contact-rich manipulation, deformable objects, and long-horizon tasks where simulation fidelity gaps cause policy failures in real-world deployment.

Hybrid pipelines combining synthetic pretraining with real-world fine-tuning show promise. RT-1 used 100% real data; RT-2 incorporated web-scale vision-language pretraining but still required 6,000 real tasks for robotics grounding^[13]. Emerging approaches pretrain on synthetic data (1M+ episodes), then fine-tune on 5K-10K real trajectories — reducing real-data requirements by 90% while maintaining deployment performance. Truelabel's marketplace supports this workflow by providing targeted real-world datasets for fine-tuning and failure-mode coverage, complementing synthetic pretraining rather than replacing it.

The optimal data mix remains task-dependent. Tabletop pick-place benefits from 80% synthetic, 20% real data; kitchen manipulation requires 60% real due to deformable objects and contact complexity; outdoor mobile manipulation demands 90% real due to lighting and terrain variability. Marketplace models enable rapid experimentation with real-data ratios, allowing teams to empirically determine the minimum real-data budget for their deployment environment — a flexibility that monolithic collection approaches (all-synthetic or all-real) cannot provide.

Choosing Between Curation and Capture Platforms

Teams should select platforms based on their primary bottleneck. If you possess large unlabeled image corpora and face annotation budget constraints, curation platforms like Lightly, Encord Active, or Dataloop deliver 30-50% cost reductions through active learning and intelligent sampling. These tools optimize existing datasets but do not generate net-new data.

If your bottleneck is capturing diverse real-world interaction data for robotics training, marketplace models like truelabel provide distributed capture infrastructure and enrichment pipelines purpose-built for physical AI. The platform's 12,000+ collectors generate teleoperation trajectories, annotated sensor streams, and multi-environment datasets at scales (10K-100K episodes) matching foundation model pretraining requirements^[2].

Hybrid approaches combine both: use marketplaces to generate base datasets, then apply curation tools to select maximally informative subsets for expensive human verification or policy evaluation. Open X-Embodiment followed this pattern, aggregating 1 million trajectories then curating 200K high-quality episodes for RT-X training^[5]. For teams building production systems, the capture bottleneck typically dominates early in projects (months 0-6), while curation optimization becomes relevant later (months 6-12) as dataset scales grow and annotation budgets tighten. Selecting the right tool for each phase accelerates overall timelines and reduces total cost of ownership.

Related pages

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

External references and source context

1. Scale AI: Expanding Our Data Engine for Physical AI

   Scale AI's physical AI platform and data engine for robotics

   scale.com ↩
2. truelabel physical AI data marketplace bounty intake

   Truelabel operates a marketplace with 12,000+ collectors for physical AI data capture

   truelabel.ai ↩
3. Model Cards for Model Reporting

   Model card documentation requirements for ML systems

   arXiv ↩
4. Encord Series C announcement

   Encord's $60M Series C and platform growth metrics

   encord.com ↩
5. Open X-Embodiment: Robotic Learning Datasets and RT-X Models

   Open X-Embodiment's 1 million trajectories across 22 datasets

   arXiv ↩
6. MCAP specification

   MCAP specification for columnar storage and schema evolution

   MCAP ↩
7. scale.com scale ai universal robots physical ai

   Scale AI and Universal Robots physical AI partnership

   scale.com ↩
8. RoboNet: Large-Scale Multi-Robot Learning

   RoboNet paper documenting 15 million frames across 7 platforms

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

   DROID paper documenting 76,000 trajectories and collection methodology

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

    LeRobot state-of-the-art machine learning for real-world robotics

    arXiv ↩
11. Datasheets for Datasets

    Datasheets for Datasets paper on transparent dataset documentation

    arXiv ↩
12. RLDS: an Ecosystem to Generate, Share and Use Datasets in Reinforcement Learning

    RLDS paper on reinforcement learning dataset ecosystem

    arXiv ↩
13. RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control

    RT-2 vision-language-action model with 6,000 tasks

    arXiv ↩
14. RoboNet dataset license

    RoboNet CC BY 4.0 license terms

    GitHub raw content ↩
15. EPIC-KITCHENS-100 annotations license

    EPIC-KITCHENS non-commercial license restrictions

    GitHub ↩
16. Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World

    Domain randomization for sim-to-real transfer

    arXiv ↩

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