Autonomous Forklift Cloth Folding Gym Environment
Cloth Folding for the Autonomous Forklift — a Gymnasium training environment. Captured/authored with joint encoders in the loop and validated against real Autonomous Forklift 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
Runs on Jetson Orin; 16GB VRAM required.
Frameworks
- ROS 2Isaac GymIsaac Sim
Hardware
- ARM Cortex-A78Velodyne VLP-16
Sensors
- joint encoders
Specs
- GPU: 16GB VRAM
- Size: 1.08 MB
- Duration: 10s
- Validation loss 0.2849 after 294 epochs
- ROS compatible
- Jetson compatible
- Isaac Sim compatible
Tags
$299
Research Only license
autonomous-forklift-cloth-folding-gym-environment.zip · 1.08 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 →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.
Related assets
Outdoor Yard Gazebo World (.sdf) — Autonomous Forklift
Waypoint Navigation for the Autonomous Forklift — a ready-to-load simulation world. Captured/authored with 2D LiDAR in the loop and validated against real Autonomous Forklift 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.