Pick-and-Place ONNX Model Bundle — Humanoid
Pick-and-Place for the Humanoid — a deployment-ready model bundle with weights, config and inference code. Captured/authored with RGB-D camera in the loop and validated against real Humanoid kinematics. Drop-in ready for training, sim-to-real transfer, or on-robot deployment.
Demonstration clipfree preview
Left: real-world source footage. Right: a signal derived from it (inter-frame motion field or edge/feature map). Source & license are credited in the clip.
Preview
Free preview — no purchase requiredDeployment
CPU/GPU host deployment; no GPU required.
Frameworks
- ONNX Runtime
Hardware
- x86-64 CPU
Sensors
- RGB-D cameraevent cameraRGB camera
Specs
- Size: 1.69 MB
- Duration: 10s
- mAP@0.5 = 0.578 on held-out set
Tags
Free
Commercial license
pick-and-place-onnx-model-bundle-humanoid.zip · 1.69 MB
ND-Dev✓
Trust score
Would you deploy this in production?
Not enough votes yet. (0/3 so far)
Sign in to vote.
Reviews
0 reviews
No reviews yet. Be the first to share your experience.
More from ND-Dev
View profile →3D LiDAR Calibration for Autonomous Mobile Robot (.yaml)
Visual Servoing for the Autonomous Mobile Robot — precise intrinsic + extrinsic calibration for the 3D LiDAR. Captured/authored with 3D LiDAR in the loop and validated against real Autonomous Mobile Robot kinematics. Drop-in ready for training, sim-to-real transfer, or on-robot deployment.
Dual-Arm Torso Screw Driving Gym Environment
Screw Driving for the Dual-Arm Torso — a Gymnasium training environment. Captured/authored with joint encoders in the loop and validated against real Dual-Arm Torso kinematics. Drop-in ready for training, sim-to-real transfer, or on-robot deployment.
Hospital Ward Gazebo World (.sdf) — Dual-Arm Torso
Waypoint Navigation for the Dual-Arm Torso — a ready-to-load simulation world. Captured/authored with 2D LiDAR in the loop and validated against real Dual-Arm Torso kinematics. Drop-in ready for training, sim-to-real transfer, or on-robot deployment.
Related assets
Drawer Manipulation ONNX Model Bundle — Dual-Arm Torso
Drawer Manipulation for the Dual-Arm Torso — a deployment-ready model bundle with weights, config and inference code. Captured/authored with RGB-D camera in the loop and validated against real Dual-Arm Torso kinematics. Drop-in ready for training, sim-to-real transfer, or on-robot deployment.
TipGuard — Bipedal Fall-Risk Detector (ONNX)
TipGuard is a lightweight (4.7 KB) ONNX classifier that flags imminent loss-of-balance on bipedal/humanoid robots from a single IMU window. Input is a 9-feature vector — accel x/y/z (g), gyro x/y/z (deg/s), roll, pitch (deg), and angular-velocity magnitude (deg/s); output is a 2-class softmax probability [stable, fall_risk]. Feature normalization is baked into the graph, so you feed raw IMU readings directly. Trained on the companion Bipedal IMU Fall-Risk Telemetry dataset (2,400 labeled 0.5 s windows) and exported at opset 17. Runs in microseconds on Jetson Orin / any ONNX Runtime target — ideal as a safety reflex node. Validated example: a stable stance returns P(fall)=0.00; a 50+ deg/s tumbling window returns P(fall)=1.00.
TipGuard — Bipedal Fall-Risk Detector (ONNX)
TipGuard is a lightweight (4.7 KB) ONNX classifier that flags imminent loss-of-balance on bipedal/humanoid robots from a single IMU window. Input is a 9-feature vector — accel x/y/z (g), gyro x/y/z (deg/s), roll, pitch (deg), and angular-velocity magnitude (deg/s); output is a 2-class softmax probability [stable, fall_risk]. Feature normalization is baked into the graph, so you feed raw IMU readings directly. Trained on the companion Bipedal IMU Fall-Risk Telemetry dataset (2,400 labeled 0.5 s windows) and exported at opset 17. Runs in microseconds on Jetson Orin / any ONNX Runtime target — ideal as a safety reflex node. Validated example: a stable stance returns P(fall)=0.00; a 50+ deg/s tumbling window returns P(fall)=1.00.