Pi0.5 LoRA — Task 002: Pick and Place Objects to X Mark
Fine-tuned Pi0.5 checkpoint using LoRA adapters for multi-object pick-and-place from a square box to an X mark.
Model Details
Parameter
Value
Base model
Pi0.5 (pi05_base)
Tuning method
LoRA (low-rank adaptation)
Framework
OpenPI + JAX
Action dim
18 (joint-space)
Action horizon
10 steps
State dim
18 (qpos)
Image inputs
head_camera (224x224) + wrist_left (224x224)
Training Configuration
Parameter
Value
GPU
NVIDIA H100 80GB HBM3
Batch size
4
Training steps
30,000
Optimizer
AdamW (grad clip norm=1.0)
LR schedule
Cosine decay, peak=5e-5, warmup=10k steps
Final loss
0.0506
Final grad norm
~0.49
Dataset
Task T-002: "Pick and Place different objects from a square box to the X Mark"
Source: s3://unix-ai-data/teleop_data/task_002/
Total episodes: 1,553
Total frames: 913,774
FPS: 40 Hz
Format: Converted from Unix-AI HDF5 to LeRobot
Objects (10 unique)
Object
Episodes
Blue box
149
Foam brick
150
C clamp
150
T clamp
150
USB cable
149
Plastic clamp
150
Wooden block
150
Screwdriver
151
Timer clock
206
Rubiks cube
148
Language Commands
Each episode is conditioned on a natural language prompt:
Pick and place blue box to the X mark
Pick and place foam brick to the X mark
Pick and place C clamp to the X mark
Pick and place T clamp to the X mark
Pick and place USB cable to the X mark
Pick and place plastic clamp to the X mark
Pick and place wooden block to the X mark
Pick and place screwdriver to the X mark
Pick and place timer clock to the X mark
Pick and place Rubiks cube to the X mark
1# Serve the model2python scripts/serve.py --config pi05_unix_ai_lora --checkpoint checkpoints/pi05_unix_ai_lora/pi05_task_002_lora/29999/params
34# Inference5from openpi_client import Client
6client = Client("http://localhost:8000")7action = client.infer(8observations={"image": img, "wrist_image": wrist_img, "state": qpos},
9prompt="Pick and place blue box to the X mark"10)
