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beatsprom/robotics-embodied-ai-physical-control-2026

🤖 Robotics, Embodied AI & Physical World Control Dataset (2026 Edition) A structured research dataset featuring 2,123 domain-verified research papers and official code repositories focused on Vision-Language-Action Models (VLA), Humanoid Robotics, Quadruped Locomotion, Diffusion Policies, Sim-to-Real Transfer, and Physics Simulation Environments (Isaac Sim, MuJoCo, Genesis). Built with Universal Scientific Engine V16.1 Gold, providing 43 schema attributes with verified… See the full description on the dataset page: https://huggingface.co/datasets/beatsprom/robotics-embodied-ai-physical-control-2026.

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Dataset Card

🤖 Robotics, Embodied AI & Physical World Control Dataset (2026 Edition)

A structured research dataset featuring 2,123 domain-verified research papers and official code repositories focused on Vision-Language-Action Models (VLA), Humanoid Robotics, Quadruped Locomotion, Diffusion Policies, Sim-to-Real Transfer, and Physics Simulation Environments (Isaac Sim, MuJoCo, Genesis).

Built with Universal Scientific Engine V16.1 Gold, providing 43 schema attributes with verified repository attribution, 8 AI topological semantic clusters, 10 policy architectures, and native 384-dimensional PyTorch embeddings.

📊 Dataset Schema Highlights (43 Columns)

FieldTypeDescription
paper_idStringUnique ArXiv identifier
titleStringResearch paper title
cluster_topic_nameString1 of 8 AI Topological Semantic Clusters
embodied_policy_architectureStringControl policy (VLA, ACT, DP3, WBC, DRL, MPC)
robot_morphology_targetStringRobot morphology (Humanoid, Multi-DoF Arm, Quadruped, Drone)
physics_simulators_supportedList[String]Simulation platforms (Isaac Sim, MuJoCo, Genesis, SAPIEN)
commercial_ip_safety_scoreInteger0–100 commercial compliance index (92% Enterprise Safe)
tldr_neural_summaryString15-word executive summary of key innovation
title_vector_384dList[Float]384d PyTorch embedding (all-MiniLM-L6-v2)
abstract_vector_384dList[Float]384d PyTorch embedding (all-MiniLM-L6-v2)
reproduction_recipeString1-line bash setup command

🧩 8 AI Semantic Clusters Breakdown

  1. 1.Sim-to-Real Transfer & Physics Simulation Environments (603 papers)
  2. 2.Dexterous Robotic Manipulation & Tactile Gripping (356 papers)
  3. 3.Bipedal Humanoid Locomotion & Whole-Body Dynamic Balance (279 papers)
  4. 4.Deep Reinforcement Learning & Reward Policy Optimization (266 papers)
  5. 5.Diffusion Policy & Imitation Teleoperation Learning (205 papers)
  6. 6.Vision-Language-Action (VLA) & Foundation Policy Models (162 papers)
  7. 7.Autonomous Mobile Navigation, SLAM & 3D Spatial Mapping (148 papers)
  8. 8.Aerial Quadrotors, Drones & Multi-Robot Swarm Control (104 papers)

💻 1-Click Python Quickstart

python
import pyarrow.parquet as pq

# Load 100-Sample Teaser
table = pq.read_table("ROBOTICS_EMBODIED_AI_PHYSICAL_WORLD_CONTROL_2026_100_SAMPLE.parquet")
df = table.to_pandas()

print(f"Loaded {len(df)} sample Embodied AI papers.")
print(f"Top Paper: {df['title'].iloc[0]} (Policy: {df['embodied_policy_architecture'].iloc[0]})")

🚀 Get the Full 2,123-Paper Enterprise Edition

The complete commercial production dataset (2,123 papers in Parquet with 384d vectors, SQLite DB, Clean CSV, and JSON) is available here:

👉 [BeatsProm Embodied AI & Robotics Dataset Full Edition](https://beatsprom.gumroad.com/l/robotics-embodied-ai-physical-control-2026)