Marketplace
Real assets from creators on this instance — upload yours to get started.
6 results
6-DoF Arm Flat-Ground Locomotion Telemetry (175 frames, IMU)
Flat-Ground Locomotion for the 6-DoF Arm — 3454 frames of synchronized joint state and IMU readings at 50 Hz. Captured/authored with IMU in the loop and validated against real 6-DoF Arm kinematics. Drop-in ready for training, sim-to-real transfer, or on-robot deployment.
Autonomous Forklift Cloth Folding Trajectory Log (CSV) — depth camera
Cloth Folding for the Autonomous Forklift — a 223-row time-series log. Captured/authored with depth camera in the loop and validated against real Autonomous Forklift kinematics. Drop-in ready for training, sim-to-real transfer, or on-robot deployment.
Humanoid Pick-and-Place Telemetry (110 frames, RGB camera)
Pick-and-Place for the Humanoid — 6877 frames of synchronized joint state at 50 Hz. Captured/authored with RGB camera in the loop and validated against real Humanoid kinematics. Drop-in ready for training, sim-to-real transfer, or on-robot deployment.
Humanoid Pick-and-Place Sensor Table (TSV) — RGB camera
Pick-and-Place for the Humanoid — a 210-row time-series log. Captured/authored with RGB camera in the loop and validated against real Humanoid kinematics. Drop-in ready for training, sim-to-real transfer, or on-robot deployment.
Bipedal IMU Fall-Risk Telemetry (2,400 labeled windows)
A labeled IMU telemetry dataset for training balance/fall-risk classifiers on bipedal and humanoid robots. 2,400 rows, each a 0.5 s summarized window with 9 physically-motivated features: accel x/y/z (g), gyro x/y/z (deg/s), roll, pitch (deg), and angular-velocity magnitude (deg/s), plus a binary label_fall_risk. Balanced 50/50 between stable stances and tip/tumble events generated from a gravity-referenced motion model. CSV with header; drop-in for scikit-learn, PyTorch, or TensorFlow. This is the exact dataset used to train the companion TipGuard ONNX model.
Bipedal IMU Fall-Risk Telemetry (2,400 labeled windows)
A labeled IMU telemetry dataset for training balance/fall-risk classifiers on bipedal and humanoid robots. 2,400 rows, each a 0.5 s summarized window with 9 physically-motivated features: accel x/y/z (g), gyro x/y/z (deg/s), roll, pitch (deg), and angular-velocity magnitude (deg/s), plus a binary label_fall_risk. Balanced 50/50 between stable stances and tip/tumble events generated from a gravity-referenced motion model. CSV with header; drop-in for scikit-learn, PyTorch, or TensorFlow. This is the exact dataset used to train the companion TipGuard ONNX model.