import os
os.environ["PRISMATIC_DATA_ROOT"] = "./"
os.environ["TOKENIZERS_PARALLELISM"] = "false"

from prismatic.models import load_vla
from deploy.mybot_deploy import MyBotDeploy

# 1. Load model
vla = load_vla("purplehihi/minivla-myCobot280pi-task2-v6", hf_token="dummy")

# 2. Load deployment controller
deploy = MyBotDeploy(stats_path="dataset_statistics.json")
deploy.set_current_encoders(current_encoders)  # from robot

# 3. Per-frame inference
action = vla.predict_action(image, instruction, unnorm_key="mycobot_task2")
encoders, gripper_pwm, info = deploy.step(action)
robot.send_angles(encoders.tolist(), speed=50)
robot.set_gripper(gripper_pwm)

Deployment Pipeline (`mybot_deploy.py`)

VLA output [dx,dy,dz,droll,dpitch,dyaw,gripper]
  → unnormalize (BOUNDS: q01/q99)
  → current EEF + delta = target EEF
  → numerical IK (damped least squares)
  → 6 joint angles → motor encoders
  → gripper [0,1] → PWM [0,100]

The IK uses damped least squares (Levenberg-Marquardt) with numerical Jacobian. Accuracy: ~0.3mm position, ~0.2° orientation (100 random tests).

Normalization Bounds

Dim	Min	Max
dx	-35	+35 mm
dy	-30	+30 mm
dz	-25	+25 mm
droll	-25	+25°
dpitch	-15	+15°
dyaw	-20	+20°
gripper	0	1 (absolute, not normalized)

Files

model.pt — Fine-tuned weights (step 60920, loss=2.7e-5)
dataset_statistics.json — Normalization stats with BOUNDS q01/q99
config.json — Training config and metadata
mybot_deploy.py — Edge deployment: VLA output → encoder commands + IK

purplehihi/minivla-myCobot280pi-task2-v6

MiniVLA — MyCobot 280 Pi Task2 (v6, Epoch 2)

Performance

Model

Quick Start

Deployment Pipeline (mybot_deploy.py)

Normalization Bounds

Files

purplehihi/
minivla-myCobot280pi-task2-v6

Deployment Pipeline (`mybot_deploy.py`)