Asimov (YC W26) Alternatives: Physical AI Data Beyond Egocentric Video

Quick facts

Topic

Asimov YC W26

Audience

Procurement leads, ML ops, robotics engineers

Deliverable

Buyer-facing reference + procurement guidance

What Asimov (YC W26) Delivers for Robotics Teams

Asimov entered Y Combinator's Winter 2026 batch with a focus on real-world human activity data for robotics training. The company distributes wearable hardware to collectors across multiple continents, capturing egocentric video paired with rich annotations: 3D body pose estimation, depth maps, semantic segmentation, and activity-level labels. This end-to-end pipeline—hardware provisioning, data collection, quality assurance, post-processing—targets teams building humanoid robots or embodied agents that learn from human demonstrations.

Asimov's annotation layers include temporal segmentation of activities (e.g., 'opening drawer,' 'pouring liquid') and spatial metadata (hand trajectories, object interactions). The egocentric viewpoint mirrors first-person perspectives common in EPIC-KITCHENS and Ego4D, datasets widely cited in vision-language-action research. However, egocentric video alone does not provide robot joint states, gripper forces, or proprioceptive feedback—signals required for manipulation policy training.

For teams prototyping vision-based imitation learning or activity recognition pipelines, Asimov's human-centric data offers a starting point. For teams deploying manipulation policies on physical robots, alternatives that capture robot teleoperation trajectories, multi-sensor fusion, and robotics-native formats (RLDS, MCAP, HDF5) deliver higher training signal per episode.

Why Physical AI Teams Need More Than Egocentric Video

Egocentric human activity datasets excel at high-level task understanding—recognizing 'chopping vegetables' or 'folding laundry'—but lack the low-level control signals robots need for manipulation. RT-1 and RT-2 trained on 130,000+ robot trajectories with joint positions, gripper states, and end-effector poses sampled at 3 Hz. Human video provides visual context; robot trajectories provide action distributions.

The Open X-Embodiment dataset aggregates 1 million trajectories across 22 robot embodiments, 527 skills, and 160,000 tasks^[1]. Each episode includes RGB-D images, proprioceptive state vectors, and action labels in a unified RLDS schema. This multi-embodiment coverage enables cross-robot generalization—a capability egocentric video cannot provide without additional instrumentation.

Physical AI buyers prioritize datasets with robot-executable actions, not human activity labels. DROID collected 76,000 manipulation trajectories across 564 scenes and 86 objects using teleoperation rigs^[2]. Each trajectory pairs RGB-D video with 6-DOF end-effector poses and binary gripper states. This action-centric structure directly trains diffusion policies and transformer-based controllers. Egocentric video requires a separate inverse-kinematics or retargeting layer to map human motion to robot commands—adding latency and error.

Manipulation Trajectory Datasets: The Gold Standard for Policy Training

Manipulation policy training demands datasets where every frame pairs observations (images, depth, proprioception) with executable actions (joint velocities, gripper commands). BridgeData V2 provides 60,000 trajectories across 13 skills and 24 environments, formatted in RLDS with per-timestep action vectors^[3]. Policies trained on BridgeData generalize to novel objects and scenes within the same task family.

LeRobot standardizes this structure across 20+ datasets, exposing a unified API for trajectory sampling, augmentation, and batching. Each episode stores RGB images, depth maps, robot state (joint angles, gripper position), and action labels (target joint velocities or end-effector deltas). This schema mirrors the RLDS specification, enabling cross-dataset training without format conversion.

Teleoperation datasets like ALOHA capture bimanual manipulation at 50 Hz, recording synchronized stereo RGB, wrist-mounted cameras, and dual-arm joint states. The 650-episode ALOHA dataset trains ACT (Action Chunking Transformer) policies that achieve 80%+ success on cable routing and dishwasher loading tasks^[4]. Egocentric human video lacks the temporal resolution and action labels required for this level of closed-loop control.

Multi-Sensor Fusion: RGB-D, LiDAR, and Point Cloud Enrichment

Physical AI systems integrate multiple sensor modalities—RGB cameras, depth sensors, LiDAR, IMUs—to build robust world models. Scale AI's physical AI data engine processes multi-sensor streams with frame-synchronized timestamps, calibrated extrinsics, and semantic annotations across all modalities. A single driving scene might pair 64-beam LiDAR point clouds with 6-camera RGB arrays and radar returns, all labeled with 3D bounding boxes and tracking IDs.

Segments.ai specializes in point cloud labeling for robotics and autonomous systems, supporting PCD, LAS, and custom binary formats. Annotators label 3D objects, ground planes, and traversability masks directly in point cloud space, avoiding the projection errors inherent in 2D-to-3D lifting. This workflow suits mobile robots navigating warehouses or outdoor environments where depth precision matters.

Asimov's egocentric pipeline captures RGB video and monocular depth estimation, but does not advertise LiDAR integration or calibrated multi-camera rigs. For teams building navigation stacks or outdoor manipulation systems, datasets with true 3D sensor fusion—like Waymo Open Dataset (1,950 scenes, 12M 3D boxes) or custom collections via truelabel's marketplace—provide the geometric fidelity required for SLAM, occupancy mapping, and collision avoidance.

Robotics-Native Formats: RLDS, MCAP, HDF5, and Parquet

Data format determines training pipeline efficiency. RLDS (Reinforcement Learning Datasets) wraps TensorFlow Datasets with a trajectory-centric schema: each episode is a sequence of (observation, action, reward) tuples stored in TFRecord shards. RLDS enables random-access sampling, on-the-fly augmentation, and distributed loading across GPU clusters. Over 30 robotics datasets—including BridgeData, RoboNet, and CALVIN—publish RLDS versions.

MCAP is a columnar container for multi-modal time-series data, designed for ROS 2 bag replacement. MCAP files store synchronized RGB, depth, LiDAR, and IMU streams with microsecond timestamps and per-message schemas. Foxglove uses MCAP as its native format, enabling browser-based playback of 100 GB+ datasets without transcoding. For teams collecting teleoperation data with ROS 2, MCAP offers 3–5× compression over rosbag2 and faster seek times.

HDF5 remains the de facto standard for large-scale manipulation datasets. DROID stores 76,000 trajectories in HDF5 with hierarchical groups: `/episode_000001/observations/rgb`, `/episode_000001/actions/joint_positions`. This structure supports partial loading (read one camera without loading depth) and parallel writes during collection. Asimov has not published format specifications; teams should confirm export compatibility with their training stack before procurement.

truelabel's Physical AI Marketplace: 12,000 Collectors, 60+ Datasets

truelabel's physical AI data marketplace connects buyers with 12,000+ verified collectors across 40 countries, specializing in robotics-specific capture: manipulation trajectories, teleoperation sessions, multi-sensor fusion, and sim-to-real validation sets^[5]. Each dataset includes provenance metadata (collector ID, hardware specs, calibration files) and licensing terms (commercial-use rights, derivative permissions, attribution requirements).

The marketplace hosts 60+ robotics datasets spanning kitchen tasks, warehouse navigation, outdoor manipulation, and bimanual assembly. Claru's kitchen task dataset provides 8,000+ teleoperation episodes across 15 appliances and 30 utensils, annotated with object affordances and failure modes. Claru's warehouse teleoperation dataset captures 12,000 pick-place-navigate sequences in real logistics facilities, paired with LiDAR maps and collision annotations.

Buyers specify requirements via bounty intake forms: task taxonomy, sensor modalities, annotation layers, episode counts, and delivery timelines. truelabel's collector network executes capture within 2–4 weeks, with QA pipelines validating timestamp synchronization, calibration accuracy, and label consistency. This procurement model suits teams needing custom datasets (e.g., 'bimanual cable routing with force feedback') that public repositories do not cover.

Scale AI's Physical AI Data Engine: Enterprise-Grade Annotation

Scale AI's physical AI data engine extends the company's annotation infrastructure to robotics and embodied AI. Scale processes multi-sensor streams (RGB-D, LiDAR, radar, IMU) with frame-synchronized labels: 3D bounding boxes, semantic segmentation, tracking IDs, and action annotations. The platform supports custom ontologies (e.g., 'grasp affordance,' 'traversability score') and active learning loops that prioritize high-uncertainty frames for human review.

Scale's partnership with Universal Robots produced 50,000+ manipulation trajectories for UR5e and UR10e arms, annotated with contact events, force profiles, and success labels^[6]. Each trajectory includes wrist-mounted RGB-D video, joint torques, and end-effector poses at 125 Hz. This dataset trains compliance controllers and contact-rich manipulation policies.

For teams with annotation budgets exceeding $100K and timelines measured in quarters, Scale's managed service delivers production-grade labels with SLA guarantees. For teams needing faster iteration or niche tasks (e.g., 'surgical tool manipulation,' 'agricultural picking'), truelabel's marketplace offers 3–5× faster turnaround via distributed collector networks.

Open X-Embodiment: 1M+ Trajectories Across 22 Robot Platforms

The Open X-Embodiment (OXE) dataset aggregates 1 million robot trajectories from 22 institutions, spanning 22 robot embodiments and 527 skills^[7]. OXE unifies heterogeneous data sources—WidowX arms, Franka Panda robots, quadrupeds, mobile manipulators—into a single RLDS schema with standardized action spaces and observation keys.

OXE enables cross-embodiment policy training: a transformer trained on OXE data generalizes to unseen robots by conditioning on embodiment tokens (robot morphology, workspace dimensions, gripper type). RT-X models trained on OXE achieve 50–70% zero-shot success on novel tasks and robots, compared to 10–20% for single-embodiment baselines^[8].

OXE's scale (1M+ episodes) and diversity (22 embodiments) make it the largest open manipulation dataset. However, OXE episodes average 10–30 seconds and focus on tabletop tasks. For long-horizon manipulation (multi-minute assembly sequences) or outdoor navigation, teams need supplementary datasets like LongBench (200+ episodes, 5–15 minute tasks) or custom collections via truelabel's marketplace.

Sim-to-Real Transfer: Bridging Synthetic and Physical Data

Simulation generates infinite training data at zero marginal cost, but sim-to-real transfer remains brittle without real-world validation sets. Domain randomization varies lighting, textures, and physics parameters during simulation to span the real-world distribution. Policies trained on randomized simulation generalize better than those trained on photorealistic single-environment sims^[9].

RLBench provides 100 simulated manipulation tasks in PyBullet with procedurally generated variations (object poses, colors, shapes). Teams train policies in RLBench, then fine-tune on 50–200 real-world episodes collected via teleoperation. This hybrid approach reduces real-world data requirements by 5–10× compared to training from scratch on physical robots.

Asimov's human activity data does not directly support sim-to-real workflows—it lacks robot state vectors and action labels. For teams building simulation pipelines, pairing RoboSuite or ManiSkill with real-world validation sets from truelabel's marketplace offers a faster path to deployment than collecting human demonstrations first.

Teleoperation Datasets: High-Intent Demonstrations for Imitation Learning

Teleoperation datasets capture expert demonstrations where humans directly control robots to complete tasks. These datasets provide the highest-intent training signal: every action is goal-directed, and failure modes are rare. ALOHA's teleoperation dataset includes 650 bimanual episodes across 7 tasks (cable routing, dishwasher loading, towel folding), with 50 Hz action sampling and synchronized stereo RGB^[10].

RoboSet's teleoperation split provides 2,000 WidowX episodes across 10 kitchen tasks, annotated with success labels and failure modes (e.g., 'gripper slip,' 'collision with table'). Each episode includes wrist-mounted RGB, third-person RGB, and 7-DOF joint positions. Policies trained on RoboSet achieve 60–75% success on held-out object instances.

truelabel's marketplace specializes in custom teleoperation collection: buyers specify tasks, environments, and robot platforms, and collectors execute demonstrations within 2–4 weeks. A recent warehouse teleoperation bounty delivered 12,000 pick-place-navigate sequences across 8 facilities, with LiDAR maps and collision annotations. This procurement model suits teams needing task-specific datasets that public repositories do not cover.

Annotation Platforms: Labelbox, Encord, V7, and Segments.ai

Teams with raw sensor data but no annotations can use third-party platforms to add labels. Labelbox supports 2D/3D bounding boxes, segmentation masks, keypoints, and video tracking across RGB, depth, and point cloud modalities. Labelbox's model-assisted labeling pre-annotates frames with foundation models (SAM, Grounding DINO), reducing human labeling time by 50–70%.

Encord specializes in video annotation with temporal interpolation: annotators label keyframes, and Encord propagates labels across intermediate frames using optical flow. This workflow suits manipulation datasets where object poses change smoothly. Encord raised $60M in Series C funding in 2024, signaling enterprise demand for video annotation infrastructure^[11].

Segments.ai focuses on multi-sensor fusion: annotators label 3D bounding boxes in LiDAR point clouds, and Segments.ai projects labels onto synchronized RGB cameras. This ensures geometric consistency across modalities—critical for sensor fusion pipelines. For teams with unlabeled teleoperation data, pairing Segments.ai with truelabel's QA workflows delivers production-ready annotations in 1–2 weeks.

Managed Data Services: Appen, CloudFactory, iMerit, and Sama

Managed data services handle end-to-end pipelines: data collection, annotation, QA, and delivery. Appen operates a global crowd of 1M+ annotators, supporting image classification, bounding boxes, and video segmentation. Appen's robotics clients include autonomous vehicle companies and warehouse automation vendors.

CloudFactory specializes in industrial robotics and autonomous vehicles, offering custom annotation workflows (e.g., 'label gripper contact points,' 'annotate traversability masks'). CloudFactory's managed teams deliver 10,000–100,000 labeled frames per week with 95%+ accuracy SLAs.

iMerit and Sama provide similar managed services, with iMerit focusing on medical and geospatial AI and Sama emphasizing ethical labor practices. For teams needing 100K+ labeled episodes, managed services offer predictable timelines and quality. For teams needing 1K–10K episodes or niche tasks, truelabel's marketplace delivers faster iteration via distributed collectors.

When to Choose Asimov vs. Alternatives

Choose Asimov if your use case prioritizes egocentric human activity data with rich semantic annotations (3D pose, depth, activity labels) and you are building vision-based activity recognition or high-level task planning systems. Asimov's end-to-end pipeline—hardware distribution, collection, QA—suits teams without in-house data infrastructure.

Choose alternatives if you need robot manipulation trajectories with executable actions (joint positions, gripper commands), multi-sensor fusion (RGB-D + LiDAR + IMU), teleoperation datasets, or robotics-native formats (RLDS, MCAP, HDF5). truelabel's marketplace delivers custom datasets in 2–4 weeks with full provenance metadata and commercial-use licensing. Scale AI offers enterprise-grade annotation with SLA guarantees for budgets exceeding $100K. Open X-Embodiment provides 1M+ trajectories across 22 robots for zero-cost exploration.

For teams prototyping manipulation policies, start with open datasets (OXE, BridgeData, DROID) to validate architectures, then procure custom teleoperation data via truelabel's marketplace to cover task-specific edge cases. For teams deploying in production, pair simulation (RLBench, ManiSkill) with real-world validation sets to minimize data collection costs while maintaining robustness.

Related pages

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

External references and source context

1. Open X-Embodiment: Robotic Learning Datasets and RT-X Models

   OXE contains 1 million trajectories across 22 robots, 527 skills, 160,000 tasks

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

   DROID collected 76,000 trajectories across 564 scenes and 86 objects

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

   BridgeData V2 spans 13 skills and 24 environments with per-timestep actions

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

   ALOHA dataset includes 650 episodes with 80%+ success on cable routing tasks

   tonyzhaozh.github.io ↩
5. truelabel physical AI data marketplace bounty intake

   truelabel operates 12,000+ collectors across 40 countries with 60+ robotics datasets

   truelabel.ai ↩
6. scale.com scale ai universal robots physical ai

   Scale-UR partnership produced 50,000+ trajectories with force profiles

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

   OXE spans 22 robot embodiments and 527 skills

   arXiv ↩
8. Open X-Embodiment: Robotic Learning Datasets and RT-X Models

   RT-X achieves 50-70% zero-shot success vs 10-20% single-embodiment baselines

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

   Randomized simulation improves real-world generalization over photorealistic sims

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

    ALOHA includes 650 episodes across 7 tasks with 50 Hz sampling

    tonyzhaozh.github.io ↩
11. Encord Series C announcement

    Encord raised $60M Series C in 2024 for video annotation infrastructure

    encord.com ↩

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