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A Coding Guide to End-to-End Robotics Learning with LeRobot: Training, Evaluating, and Visualizing Behavior Cloning Policies on PushT

ByRicardo

In this tutorial, we stroll step-by-step by means of utilizing Hugging Face’s LeRobot library to practice and consider a behavior-cloning coverage on the PushT dataset. We start by establishing the atmosphere in Google Colab, putting in the required dependencies, and loading the dataset by means of LeRobot’s unified API. We then design a compact visuomotor coverage that mixes a convolutional spine with a small MLP head, permitting us to map picture and state observations straight to robotic actions. By coaching on a subset of the dataset for velocity, we’re ready to rapidly reveal how LeRobot permits reproducible, dataset-driven robotic studying pipelines. Check out the FULL CODES here.

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!pip -q set up --upgrade lerobot torch torchvision timm imageio[ffmpeg]


import os, math, random, io, sys, json, pathlib, time
import torch, torch.nn as nn, torch.nn.useful as F
from torch.utils.information import DataLoader, Subset
from torchvision.utils import make_grid, save_image
import numpy as np
import imageio.v2 as imageio


attempt:
   from lerobot.widespread.datasets.lerobot_dataset import LeRobotDataset
besides 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 putting in the required libraries and establishing the environment for coaching. We import all of the important modules, configure the dataset loader, and repair the random seed to guarantee reproducibility. We additionally detect whether or not we’re operating on a GPU or CPU, permitting our experiments to run effectively. Check out the FULL CODES here.

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REPO_ID = "lerobot/pusht" 
ds = LeRobotDataset(REPO_ID) 
print("Dataset size:", len(ds))


s0 = ds[0]
keys = listing(s0.keys())
print("Sample keys:", keys)


def key_with(prefixes):
   for ok in keys:
       for p in prefixes:
           if ok.startswith(p): return ok
   return None


K_IMG = key_with(["observation.image", "observation.images", "observation.rgb"])
K_STATE = key_with(["observation.state"])
K_ACT = "motion"
assert K_ACT in s0, f"No 'motion' key present in pattern. Found: {keys}"
print("Using keys -> IMG:", K_IMG, "STATE:", K_STATE, "ACT:", K_ACT)

We load the PushT dataset with LeRobot and examine its construction. We verify the out there keys, establish which of them correspond to pictures, states, and actions, and map them for constant entry all through our coaching pipeline. Check out the FULL CODES here.

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class PushTWrapper(torch.utils.information.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.form[-2:] != (96,96):
           img = F.interpolate(img.unsqueeze(0), measurement=(96,96), mode="bilinear", align_corners=False)[0]
       return {"picture": img, "state": state, "motion": act}


wrapped = PushTWrapper(ds)
N = len(wrapped)
idx = listing(vary(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 wrap every pattern so we persistently get a normalized 96×96 picture, a flattened state, and an motion, choosing the final body if a temporal stack is current. We then shuffle, cut up into practice/val, and cap sizes for quick Colab runs. Finally, we create environment friendly DataLoaders with batching, shuffling, and pinned reminiscence to preserve coaching easy. Check out the FULL CODES here.

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class SmallBackbone(nn.Module):
   def __init__(self, out=256):
       tremendous().__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 ahead(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):
       tremendous().__init__()
       self.spine = 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 ahead(self, img, state):
       z = self.spine(img)
       if state.ndim==1: state = state.unsqueeze(0)
       z = torch.cat([z, state], dim=-1)
       return self.mlp(z)


coverage = BCPolicy().to(DEVICE)
choose = torch.optim.AdamW(coverage.parameters(), lr=3e-4, weight_decay=1e-4)
scaler = torch.cuda.amp.GradScaler(enabled=(DEVICE=="cuda"))


@torch.no_grad()
def consider():
   coverage.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 = coverage(img, st)
       mse += F.mse_loss(pred, act, discount="sum").merchandise()
       n += act.numel()
   return mse / n


def cosine_lr(step, complete, base=3e-4, min_lr=3e-5):
   if step>=complete: return min_lr
   cos = 0.5*(1+math.cos(math.pi*step/complete))
   return min_lr + (base-min_lr)*cos


EPOCHS = 4 
steps_total = EPOCHS*len(train_loader)
step = 0
greatest = float("inf")
ckpt = "/content material/lerobot_pusht_bc.pt"


for epoch in vary(EPOCHS):
   coverage.practice()
   for batch in train_loader:
       lr = cosine_lr(step, steps_total); step += 1
       for g in choose.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)


       choose.zero_grad(set_to_none=True)
       with torch.cuda.amp.autocast(enabled=(DEVICE=="cuda")):
           pred = coverage(img, st)
           loss = F.smooth_l1_loss(pred, act)
       scaler.scale(loss).backward()
       nn.utils.clip_grad_norm_(coverage.parameters(), 1.0)
       scaler.step(choose); scaler.replace()


   val_mse = consider()
   print(f"Epoch {epoch+1}/{EPOCHS} | Val MSE: {val_mse:.6f}")
   if val_mse < greatest:
       greatest = val_mse
       torch.save({"state_dict": coverage.state_dict(), "val_mse": greatest}, ckpt)


print("Best Val MSE:", greatest, "| Saved:", ckpt)

We outline a compact visuomotor coverage: a CNN spine extracts picture options that we fuse with the robotic state to predict 2-D actions. We practice with AdamW, a cosine learning-rate schedule, blended precision, and gradient clipping, whereas evaluating with MSE on the validation set. We checkpoint the most effective mannequin by validation loss so we will reload the strongest coverage later. Check out the FULL CODES here.

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coverage.load_state_dict(torch.load(ckpt)["state_dict"]); coverage.eval()
os.makedirs("/content material/vis", exist_ok=True)


def draw_arrow(imgCHW, action_xy, scale=40):
   import PIL.Image, PIL.ImageDraw
   C,H,W = imgCHW.form
   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 vary(60):
       b = wrapped[i]
       img = b["image"].unsqueeze(0).to(DEVICE)
       st  = b["state"].unsqueeze(0).to(DEVICE)
       pred = coverage(img, st)[0].cpu()
       frames.append(draw_arrow(b["image"], pred))
video_path = "/content material/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 vary(16)]), nrow=8)
save_image(grid, "/content material/vis/grid.png")
print("Saved grid:", "/content material/vis/grid.png")

We reload the most effective checkpoint and change the coverage to eval so we will visualize its habits. We overlay predicted motion arrows on frames, sew them into a brief MP4, and additionally save a fast picture grid for a snapshot view of the dataset. This lets us verify, at a look, what actions our mannequin outputs on actual PushT observations.

In conclusion, we see how simply LeRobot integrates information dealing with, coverage definition, and analysis right into a single framework. By coaching our light-weight coverage and visualizing predicted actions on PushT frames, we verify that the library provides us a sensible entry level into robotic studying with no need real-world {hardware}. We are actually geared up to lengthen the pipeline to extra superior fashions, similar to diffusion or ACT insurance policies, to experiment with completely different datasets, and even to share our skilled insurance policies on the Hugging Face Hub.

Check out the FULL CODES here. Feel free to try our GitHub Page for Tutorials, Codes and Notebooks. Also, be happy to observe us on Twitter and don’t overlook to be a part of our 100k+ ML SubReddit and Subscribe to our Newsletter.

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