Guide that is coded for storage of robotic learning of Lerobot: training, evaluation, and visualizing social policies by pressing pusht
In this lesson, we walk the step by using a kiss under the face of the face Lerobot Training and Cyloning Global Policy Bonder In Pusht Database. We start with the environment in Google Colab, entering the required addals, and uploading data via a united API of Lerobot. We have designed Compact VisuamotoMotomotomoto Policy that includes a small milp head of the MLP, which allow us to be a picture map and state views directly in robot action. By training in the speed data store, we are able to immediately show that the lerobot enables robot-driven robots. Look Full codes here.
!pip -q install --upgrade lerobot torch torchvision timm imageio[ffmpeg]
import os, math, random, io, sys, json, pathlib, time
import torch, torch.nn as nn, torch.nn.functional as F
from torch.utils.data import DataLoader, Subset
from torchvision.utils import make_grid, save_image
import numpy as np
import imageio.v2 as imageio
try:
from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
except Exception:
from lerobot.datasets.lerobot_dataset import LeRobotDataset
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
SEED = 42
random.seed(SEED); np.random.seed(SEED); torch.manual_seed(SEED)We start by installing the required libraries and supports our environment for training. We import all essential modules, prepare the Dataset Loader, and fix random seed to ensure recycling. We also see if we are working on GPU or CPU, allowing our exams to run well. Look Full codes here.
REPO_ID = "lerobot/pusht"
ds = LeRobotDataset(REPO_ID)
print("Dataset length:", len(ds))
s0 = ds[0]
keys = list(s0.keys())
print("Sample keys:", keys)
def key_with(prefixes):
for k in keys:
for p in prefixes:
if k.startswith(p): return k
return None
K_IMG = key_with(["observation.image", "observation.images", "observation.rgb"])
K_STATE = key_with(["observation.state"])
K_ACT = "action"
assert K_ACT in s0, f"No 'action' key found in sample. Found: {keys}"
print("Using keys -> IMG:", K_IMG, "STATE:", K_STATE, "ACT:", K_ACT)We upload Disht Dataset with lerobot and check its building. We examine existing keys, assess which ones are related to photographs, provinces, and verbs, and matters in consistent access to all our training pipe. Look Full codes here.
class PushTWrapper(torch.utils.data.Dataset):
def __init__(self, base):
self.base = base
def __len__(self): return len(self.base)
def __getitem__(self, i):
x = self.base[i]
img = x[K_IMG]
if img.ndim == 4: img = img[-1]
img = img.float() / 255.0 if img.dtype==torch.uint8 else img.float()
state = x.get(K_STATE, torch.zeros(2))
state = state.float().reshape(-1)
act = x[K_ACT].float().reshape(-1)
if img.shape[-2:] != (96,96):
img = F.interpolate(img.unsqueeze(0), size=(96,96), mode="bilinear", align_corners=False)[0]
return {"image": img, "state": state, "action": act}
wrapped = PushTWrapper(ds)
N = len(wrapped)
idx = list(range(N))
random.shuffle(idx)
n_train = int(0.9*N)
train_idx, val_idx = idx[:n_train], idx[n_train:]
train_ds = Subset(wrapped, train_idx[:12000])
val_ds = Subset(wrapped, val_idx[:2000])
BATCH = 128
train_loader = DataLoader(train_ds, batch_size=BATCH, shuffle=True, num_workers=2, pin_memory=True)
val_loader = DataLoader(val_ds, batch_size=BATCH, shuffle=False, num_workers=2, pin_memory=True)We threatened each sample so we find a 96 × 96 image, a soft scenery, and the verb, weeping in the last structure in a temporary stack. We changed, separated from the train / eval, and the size of the cap for quick Colob run. Finally, we create well-working dataloers with closure, chase, and memory identified to continue smooth training. Look Full codes here.
class SmallBackbone(nn.Module):
def __init__(self, out=256):
super().__init__()
self.conv = nn.Sequential(
nn.Conv2d(3, 32, 5, 2, 2), nn.ReLU(inplace=True),
nn.Conv2d(32, 64, 3, 2, 1), nn.ReLU(inplace=True),
nn.Conv2d(64,128, 3, 2, 1), nn.ReLU(inplace=True),
nn.Conv2d(128,128,3, 1, 1), nn.ReLU(inplace=True),
)
self.head = nn.Sequential(nn.AdaptiveAvgPool2d(1), nn.Flatten(), nn.Linear(128, out), nn.ReLU(inplace=True))
def forward(self, x): return self.head(self.conv(x))
class BCPolicy(nn.Module):
def __init__(self, img_dim=256, state_dim=2, hidden=256, act_dim=2):
super().__init__()
self.backbone = SmallBackbone(img_dim)
self.mlp = nn.Sequential(
nn.Linear(img_dim + state_dim, hidden), nn.ReLU(inplace=True),
nn.Linear(hidden, hidden//2), nn.ReLU(inplace=True),
nn.Linear(hidden//2, act_dim)
)
def forward(self, img, state):
z = self.backbone(img)
if state.ndim==1: state = state.unsqueeze(0)
z = torch.cat([z, state], dim=-1)
return self.mlp(z)
policy = BCPolicy().to(DEVICE)
opt = torch.optim.AdamW(policy.parameters(), lr=3e-4, weight_decay=1e-4)
scaler = torch.cuda.amp.GradScaler(enabled=(DEVICE=="cuda"))
@torch.no_grad()
def evaluate():
policy.eval()
mse, n = 0.0, 0
for batch in val_loader:
img = batch["image"].to(DEVICE, non_blocking=True)
st = batch["state"].to(DEVICE, non_blocking=True)
act = batch["action"].to(DEVICE, non_blocking=True)
pred = policy(img, st)
mse += F.mse_loss(pred, act, reduction="sum").item()
n += act.numel()
return mse / n
def cosine_lr(step, total, base=3e-4, min_lr=3e-5):
if step>=total: return min_lr
cos = 0.5*(1+math.cos(math.pi*step/total))
return min_lr + (base-min_lr)*cos
EPOCHS = 4
steps_total = EPOCHS*len(train_loader)
step = 0
best = float("inf")
ckpt = "/content/lerobot_pusht_bc.pt"
for epoch in range(EPOCHS):
policy.train()
for batch in train_loader:
lr = cosine_lr(step, steps_total); step += 1
for g in opt.param_groups: g["lr"] = lr
img = batch["image"].to(DEVICE, non_blocking=True)
st = batch["state"].to(DEVICE, non_blocking=True)
act = batch["action"].to(DEVICE, non_blocking=True)
opt.zero_grad(set_to_none=True)
with torch.cuda.amp.autocast(enabled=(DEVICE=="cuda")):
pred = policy(img, st)
loss = F.smooth_l1_loss(pred, act)
scaler.scale(loss).backward()
nn.utils.clip_grad_norm_(policy.parameters(), 1.0)
scaler.step(opt); scaler.update()
val_mse = evaluate()
print(f"Epoch {epoch+1}/{EPOCHS} | Val MSE: {val_mse:.6f}")
if val_mse < best:
best = val_mse
torch.save({"state_dict": policy.state_dict(), "val_mse": best}, ckpt)
print("Best Val MSE:", best, "| Saved:", ckpt)It describes the Pusuomotor's PUMOTHOTOR policy: CNN Backbite issues the features of the image covering with robot state for predicting 2-D verbs. We are training for Adamw, a study program of the Cosine reading, a mixed accuracy, and determination of titles, while analyzing MSE in the prescribed MSE. We look at the best model for the loss of verification so we can relieve a powerful policy later. Look Full codes here.
policy.load_state_dict(torch.load(ckpt)["state_dict"]); policy.eval()
os.makedirs("/content/vis", exist_ok=True)
def draw_arrow(imgCHW, action_xy, scale=40):
import PIL.Image, PIL.ImageDraw
C,H,W = imgCHW.shape
arr = (imgCHW.clamp(0,1).permute(1,2,0).cpu().numpy()*255).astype(np.uint8)
im = PIL.Image.fromarray(arr)
dr = PIL.ImageDraw.Draw(im)
cx, cy = W//2, H//2
dx, dy = float(action_xy[0])*scale, float(-action_xy[1])*scale
dr.line((cx, cy, cx+dx, cy+dy), width=3, fill=(0,255,0))
return np.array(im)
frames = []
with torch.no_grad():
for i in range(60):
b = wrapped[i]
img = b["image"].unsqueeze(0).to(DEVICE)
st = b["state"].unsqueeze(0).to(DEVICE)
pred = policy(img, st)[0].cpu()
frames.append(draw_arrow(b["image"], pred))
video_path = "/content/vis/pusht_pred.mp4"
imageio.mimsave(video_path, frames, fps=10)
print("Wrote", video_path)
grid = make_grid(torch.stack([wrapped[i]["image"] for i in range(16)]), nrow=8)
save_image(grid, "/content/vis/grid.png")
print("Saved grid:", "/content/vis/grid.png")We are re-uploads the best checkpoint and switch to the policy to save it so that we can visualize its performance. We overly include action arrows in frames, send it to a short MP4, and save the speedy picture grid of data summary. This allows us to confirm, glance, what actions of our model results in the real pusht view.
In conclusion, we see how easily Lerobot connects to manage data, policy description, and testing on one framework. By training our unwavering policy and visits the actions foretold in pusht frames, we ensure that the library gives us a valid entrance to Robots. We are now intended to extend the pipe to developed models, such as disorder or legal policies, to try different datasets, and share our trained policies at Gugging Face Hub.
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