Video Joint Embedding Predictive Architecture (V-JEPA) represents a fundamental shift in how AI systems learn about the world. Instead of predicting pixels, V-JEPA predicts abstract representations of future video states, enabling the emergence of sophisticated world models that understand physics, causality, and object permanence.
Traditional video prediction models try to generate future pixelsโan extremely difficult task that often produces blurry, unrealistic results. V-JEPA takes a radically different approach: predict abstract feature representations of future video states, allowing the model to learn rich world models without getting bogged down in pixel-level details.
Compare traditional pixel prediction vs V-JEPA representation prediction:
What is V-JEPA? V-JEPA (Video Joint Embedding Predictive Architecture) is an AI system that learns to understand how the world works by watching videos - but in a very clever way.
V-JEPA uses sophisticated masking patterns to learn temporal and spatial relationships:
import torch
import torch.nn as nn
import torch.nn.functional as F
class VJEPAContextEncoder(nn.Module):
"""
Context encoder for V-JEPA architecture
Processes visible video patches to create context representations
"""
def __init__(self,
patch_size=16,
embed_dim=768,
num_heads=12,
num_layers=12,
temporal_patch_size=2):
super().__init__()
self.patch_size = patch_size
self.temporal_patch_size = temporal_patch_size
self.embed_dim = embed_dim
# Patch embedding for video
self.patch_embed = VideoPatchEmbed(
patch_size=patch_size,
temporal_patch_size=temporal_patch_size,
embed_dim=embed_dim
)
# Positional embeddings
self.pos_embed_spatial = nn.Parameter(torch.randn(1, 196, embed_dim) * 0.02) # 14x14 spatial
self.pos_embed_temporal = nn.Parameter(torch.randn(1, 8, embed_dim) * 0.02) # 8 temporal
# Transformer blocks
self.blocks = nn.ModuleList([
VisionTransformerBlock(
dim=embed_dim,
num_heads=num_heads,
mlp_ratio=4.0,
drop_path=0.1 * i / num_layers
)
for i in range(num_layers)
])
# Layer normalization
self.norm = nn.LayerNorm(embed_dim)
# Initialize weights
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
torch.nn.init.xavier_uniform_(m.weight)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
def forward(self, video_patches, mask=None):
"""
Forward pass of context encoder
Args:
video_patches: [B, T, N, D] - batched video patches
mask: [B, T, N] - masking pattern (1=visible, 0=masked)
Returns:
context_features: [B, T*N_visible, D] - context representations
"""
B, T, N, D = video_patches.shape
# Apply patch embedding
x = self.patch_embed(video_patches) # [B, T*N, D]
# Add positional embeddings
# Spatial position embedding
spatial_pos = self.pos_embed_spatial.unsqueeze(1).repeat(1, T, 1, 1) # [1, T, N, D]
spatial_pos = spatial_pos.view(1, T*N, D)
# Temporal position embedding
temporal_pos = self.pos_embed_temporal.unsqueeze(2).repeat(1, 1, N, 1) # [1, T, N, D]
temporal_pos = temporal_pos.view(1, T*N, D)
# Add both positional embeddings
x = x + spatial_pos + temporal_pos
# Apply mask if provided (keep only visible patches)
if mask is not None:
mask_flat = mask.view(B, T*N) # [B, T*N]
visible_indices = mask_flat.nonzero(as_tuple=True)
x = x[visible_indices[0], visible_indices[1]].view(B, -1, D)
# Apply transformer blocks
for block in self.blocks:
x = block(x)
# Final normalization
x = self.norm(x)
return x
class VideoPatchEmbed(nn.Module):
"""
Video patch embedding layer
Converts video patches to embedding vectors
"""
def __init__(self, patch_size=16, temporal_patch_size=2, embed_dim=768):
super().__init__()
self.patch_size = patch_size
self.temporal_patch_size = temporal_patch_size
# 3D convolution for spatiotemporal patch embedding
self.proj = nn.Conv3d(
in_channels=3,
out_channels=embed_dim,
kernel_size=(temporal_patch_size, patch_size, patch_size),
stride=(temporal_patch_size, patch_size, patch_size)
)
def forward(self, x):
"""
Args:
x: [B, T, H, W, C] video tensor
Returns:
patches: [B, T*N, D] flattened patch embeddings
"""
B, T, H, W, C = x.shape
# Rearrange for 3D convolution: [B, C, T, H, W]
x = x.permute(0, 4, 1, 2, 3)
# Apply 3D convolution
x = self.proj(x) # [B, embed_dim, T', H', W']
# Flatten spatial and temporal dimensions
x = x.flatten(2).transpose(1, 2) # [B, T'*H'*W', embed_dim]
return x
class VisionTransformerBlock(nn.Module):
"""
Standard Vision Transformer block with attention and MLP
"""
def __init__(self, dim, num_heads, mlp_ratio=4.0, drop_path=0.0):
super().__init__()
self.norm1 = nn.LayerNorm(dim)
self.attn = nn.MultiheadAttention(dim, num_heads, batch_first=True)
self.norm2 = nn.LayerNorm(dim)
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = nn.Sequential(
nn.Linear(dim, mlp_hidden_dim),
nn.GELU(),
nn.Linear(mlp_hidden_dim, dim)
)
self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
def forward(self, x):
# Self-attention with residual connection
x = x + self.drop_path(self.attn(self.norm1(x), self.norm1(x), self.norm1(x))[0])
# MLP with residual connection
x = x + self.drop_path(self.mlp(self.norm2(x)))
return x
class DropPath(nn.Module):
"""Drop paths (Stochastic Depth) per sample"""
def __init__(self, drop_prob=None):
super().__init__()
self.drop_prob = drop_prob
def forward(self, x):
if self.drop_prob == 0.0 or not self.training:
return x
keep_prob = 1 - self.drop_prob
shape = (x.shape[0],) + (1,) * (x.ndim - 1)
random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)
random_tensor.floor_()
output = x.div(keep_prob) * random_tensor
return output
class VJEPAPredictor(nn.Module):
"""
Predictor network for V-JEPA
Predicts target representations from context representations
"""
def __init__(self,
embed_dim=768,
num_heads=12,
num_layers=6,
predictor_embed_dim=384):
super().__init__()
self.embed_dim = embed_dim
self.predictor_embed_dim = predictor_embed_dim
# Project context features to predictor dimension
self.context_proj = nn.Linear(embed_dim, predictor_embed_dim)
# Mask token for target positions
self.mask_token = nn.Parameter(torch.randn(1, 1, predictor_embed_dim) * 0.02)
# Position embeddings for target locations
self.pos_embed = nn.Parameter(torch.randn(1, 2048, predictor_embed_dim) * 0.02)
# Transformer blocks for prediction
self.blocks = nn.ModuleList([
VisionTransformerBlock(
dim=predictor_embed_dim,
num_heads=num_heads,
mlp_ratio=4.0,
drop_path=0.1 * i / num_layers
)
for i in range(num_layers)
])
# Layer norm
self.norm = nn.LayerNorm(predictor_embed_dim)
# Project back to target embedding dimension
self.target_proj = nn.Linear(predictor_embed_dim, embed_dim)
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
torch.nn.init.xavier_uniform_(m.weight)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
def forward(self, context_features, target_mask, context_positions, target_positions):
"""
Predict target representations from context
Args:
context_features: [B, N_ctx, D] - context patch features
target_mask: [B, N_target] - mask for target positions
context_positions: [B, N_ctx] - position indices for context
target_positions: [B, N_target] - position indices for targets
Returns:
predicted_targets: [B, N_target, D] - predicted target features
"""
B, N_ctx, D = context_features.shape
N_target = target_positions.shape[1]
# Project context features to predictor dimension
context_embed = self.context_proj(context_features) # [B, N_ctx, predictor_dim]
# Add positional embeddings to context
context_pos_embed = self.pos_embed[:, context_positions].expand(B, -1, -1)
context_embed = context_embed + context_pos_embed
# Create mask tokens for target positions
mask_tokens = self.mask_token.expand(B, N_target, -1) # [B, N_target, predictor_dim]
# Add positional embeddings to mask tokens
target_pos_embed = self.pos_embed[:, target_positions].expand(B, -1, -1)
mask_tokens = mask_tokens + target_pos_embed
# Concatenate context and mask tokens
x = torch.cat([context_embed, mask_tokens], dim=1) # [B, N_ctx + N_target, predictor_dim]
# Apply transformer blocks
for block in self.blocks:
x = block(x)
# Extract predictions for target positions
predicted_targets = x[:, N_ctx:] # [B, N_target, predictor_dim]
# Apply layer norm and project back to target dimension
predicted_targets = self.norm(predicted_targets)
predicted_targets = self.target_proj(predicted_targets) # [B, N_target, embed_dim]
return predicted_targets
class VJEPATargetEncoder(nn.Module):
"""
Target encoder for V-JEPA (EMA of context encoder)
Encodes ground truth target patches
"""
def __init__(self, context_encoder, momentum=0.996):
super().__init__()
# Copy context encoder architecture
self.encoder = context_encoder
self.momentum = momentum
# Initialize with context encoder weights
for param_target, param_context in zip(self.encoder.parameters(),
context_encoder.parameters()):
param_target.data.copy_(param_context.data)
param_target.requires_grad = False
def update_target_network(self, context_encoder):
"""
Update target encoder weights using exponential moving average
"""
for param_target, param_context in zip(self.encoder.parameters(),
context_encoder.parameters()):
param_target.data = param_target.data * self.momentum + \
param_context.data * (1.0 - self.momentum)
def forward(self, video_patches):
"""
Forward pass through target encoder
Args:
video_patches: [B, T, N, D] target video patches
Returns:
target_features: [B, T*N, D] target representations
"""
with torch.no_grad(): # No gradients for target encoder
return self.encoder(video_patches)
def train_vjepa_step(model, video_batch, optimizer, device):
"""
Single training step for V-JEPA
Args:
model: VJEPAModel containing all components
video_batch: [B, T, H, W, C] batch of videos
optimizer: optimizer for trainable parameters
device: training device
Returns:
loss: prediction loss value
"""
B, T, H, W, C = video_batch.shape
video_batch = video_batch.to(device)
# Generate masking pattern
context_mask, target_mask, context_positions, target_positions = \
generate_masking_pattern(B, T, H//16, W//16, mask_ratio=0.4)
# Convert video to patches
video_patches = model.patch_embed(video_batch) # [B, T*N, D]
video_patches = video_patches.view(B, T, -1, model.embed_dim)
# Apply context mask and encode
context_patches = video_patches * context_mask.unsqueeze(-1)
context_features = model.context_encoder(context_patches, context_mask)
# Get target patches and encode with EMA encoder
target_patches = video_patches * target_mask.unsqueeze(-1)
with torch.no_grad():
target_features = model.target_encoder(target_patches)
# Extract only the masked target positions
target_indices = target_mask.nonzero(as_tuple=True)
target_features = target_features[target_indices[0], target_indices[1]]
# Predict target features from context
predicted_targets = model.predictor(
context_features=context_features,
target_mask=target_mask,
context_positions=context_positions,
target_positions=target_positions
)
# Compute prediction loss
loss = F.mse_loss(predicted_targets, target_features.detach())
# Add regularization terms
reg_loss = compute_regularization_loss(model)
total_loss = loss + 0.01 * reg_loss
# Backward pass
optimizer.zero_grad()
total_loss.backward()
optimizer.step()
# Update target encoder with EMA
model.update_target_encoder()
return total_loss.item()
def generate_masking_pattern(batch_size, num_frames, height, width,
mask_ratio=0.4, temporal_mask_ratio=0.5):
"""
Generate sophisticated masking patterns for V-JEPA training
Returns:
context_mask: [B, T, H, W] - 1 for context, 0 for masked
target_mask: [B, T, H, W] - 1 for prediction targets, 0 for ignored
context_positions: [B, N_context] - position indices for context
target_positions: [B, N_target] - position indices for targets
"""
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# Initialize masks
context_mask = torch.zeros(batch_size, num_frames, height, width, device=device)
target_mask = torch.zeros(batch_size, num_frames, height, width, device=device)
for b in range(batch_size):
# Temporal masking strategy
if torch.rand(1) < temporal_mask_ratio:
# Mask temporal blocks
context_frames = torch.randperm(num_frames)[:int(num_frames * (1 - mask_ratio))]
target_frames = torch.randperm(num_frames)[:int(num_frames * mask_ratio)]
context_mask[b, context_frames] = 1
target_mask[b, target_frames] = 1
else:
# Spatial masking within frames
for t in range(num_frames):
total_patches = height * width
num_context = int(total_patches * (1 - mask_ratio))
# Random spatial patches for context
context_indices = torch.randperm(total_patches)[:num_context]
context_h = context_indices // width
context_w = context_indices % width
context_mask[b, t, context_h, context_w] = 1
# Remaining patches as targets
target_indices = torch.randperm(total_patches)[:int(total_patches * mask_ratio)]
target_h = target_indices // width
target_w = target_indices % width
target_mask[b, t, target_h, target_w] = 1
# Generate position indices
context_positions = context_mask.nonzero()[:, -2:] # [N_context, 2] (h, w coordinates)
target_positions = target_mask.nonzero()[:, -2:] # [N_target, 2]
return context_mask, target_mask, context_positions, target_positions
def compute_regularization_loss(model):
"""
Compute regularization losses for stable training
"""
reg_loss = 0.0
# L2 regularization on predictor parameters
for param in model.predictor.parameters():
reg_loss += torch.sum(param ** 2)
# Feature variance regularization to prevent collapse
# (Additional regularization terms can be added here)
return reg_loss
Now let's dive into the detailed mathematics of how V-JEPA processes video data through its four main components: patch tokenization, context encoding, prediction, and target encoding (training only).
Explore each step of V-JEPA's mathematical processing pipeline:
V-JEPA shares some conceptual similarities with CLIP (Contrastive Language-Image Pre-training), but focuses on temporal understanding rather than cross-modal alignment. Both learn joint embeddings, but for very different purposes.
# CLIP-Style Approach
class CLIPStyle:
def forward(self, images, texts):
# Encode both modalities to same embedding space
image_features = self.image_encoder(images)
text_features = self.text_encoder(texts)
# Compute similarity/alignment
similarity = cosine_similarity(image_features, text_features)
# Contrastive learning: maximize correct pairs, minimize wrong pairs
loss = contrastive_loss(similarity, labels)
return loss
# V-JEPA Approach
class VJEPAStyle:
def forward(self, past_video, future_video, mask):
# Encode context from past
context_features = self.context_encoder(past_video, ~mask)
# Predict future representations
predicted_features = self.predictor(context_features, mask)
# Encode actual future (target)
target_features = self.target_encoder(future_video)
# Predictive learning: minimize prediction error
loss = mse_loss(predicted_features, target_features)
return loss
# Combined Approach
class MultiModalVJEPA:
def __init__(self):
self.clip_encoder = CLIPEncoder() # Semantic understanding
self.vjepa_predictor = VJEPAPredictor() # Temporal prediction
def forward(self, video_context, text_instruction):
# CLIP: Understand semantic goal
semantic_goal = self.clip_encoder.encode_text(text_instruction)
current_state = self.clip_encoder.encode_image(video_context)
# V-JEPA: Predict how to achieve goal
future_prediction = self.vjepa_predictor(
context=current_state,
goal=semantic_goal
)
return future_prediction
Important: During inference, V-JEPA does NOT generate video pixels. Instead, it outputs abstract feature representations that encode understanding about future states. The target encoder is also not needed during inference.
class V_JEPA_Inference:
"""
V-JEPA model optimized for inference
Only includes components needed for prediction
"""
def __init__(self, model_path):
# Load only inference components
checkpoint = torch.load(model_path)
self.context_encoder = ContextEncoder() # โ
NEEDED
self.predictor = PredictorNetwork() # โ
NEEDED
# self.target_encoder = ... # โ NOT LOADED
self.context_encoder.load_state_dict(checkpoint['context_encoder'])
self.predictor.load_state_dict(checkpoint['predictor'])
# Set to evaluation mode
self.context_encoder.eval()
self.predictor.eval()
def predict(self, input_video, prediction_positions):
"""
Generate feature predictions from video context
Args:
input_video: [B, T, H, W, C] - input video frames
prediction_positions: positions to predict
Returns:
predicted_features: [B, N_pred, D] - abstract features
"""
with torch.no_grad(): # No gradients needed for inference
# Step 1: Encode context
context_features = self.context_encoder(input_video)
# Step 2: Generate predictions
predicted_features = self.predictor(
context_features,
prediction_positions
)
# Return abstract features, NOT pixels!
return predicted_features
def get_world_model_state(self, video_context):
"""
Extract world model understanding from video
Returns abstract physics and object state representations
"""
features = self.predict(video_context, "future_states")
# Features encode concepts like:
# - Object positions and velocities
# - Physics state (momentum, potential energy)
# - Spatial relationships
# - Temporal dynamics
return features # Shape: [batch, features, embedding_dim]
display_video(v_jepa_output) โ - Output is not pixelssave_as_mp4(v_jepa_features) โ - Features are abstract numbersshow_image(predicted_frame) โ - No visual frames generated# โ INCORRECT USAGE - V-JEPA doesn't output pixels
v_jepa = V_JEPA_Model()
predicted_video = v_jepa.predict(input_video) # This gives features, not video!
cv2.imshow("prediction", predicted_video) # Will fail - not image data
# โ
CORRECT USAGE - Use features for understanding
v_jepa = V_JEPA_Model()
future_features = v_jepa.predict(input_video) # Shape: [batch, patches, 768]
# Use features for robot planning
def plan_robot_action(current_video):
world_model_features = v_jepa.predict(current_video)
# Features encode physics understanding like:
# - Object will move from position A to B
# - Collision will occur at time T
# - Surface friction affects trajectory
# Use this understanding for planning
best_action = planning_algorithm(world_model_features)
return best_action
# Use features for downstream tasks
action_classifier = ActionClassifier()
predicted_action = action_classifier(future_features)
physics_analyzer = PhysicsAnalyzer()
motion_state = physics_analyzer(future_features)
# Condition generative models
if need_visual_output:
diffusion_model = VideoDiffusionModel()
generated_video = diffusion_model.generate(
conditioning=future_features # Use V-JEPA features as guidance
)
By learning to predict abstract representations of future video states, V-JEPA develops sophisticated understanding of physical laws, object permanence, and causal relationshipsโall without explicit supervision about physics.
See how V-JEPA learns to model different physical phenomena:
V-JEPA's world modeling capabilities make it particularly valuable for robotics applications. By understanding how the world changes over time, robots can plan actions more effectively and predict the consequences of their behaviors.
Simulate how V-JEPA world models enable robot planning:
import torch
import torch.nn as nn
import numpy as np
class VJEPAWorldModel(nn.Module):
"""
World model using V-JEPA for robot planning
Predicts future states given current state and actions
"""
def __init__(self, vjepa_model, action_dim=7, state_dim=768):
super().__init__()
self.vjepa = vjepa_model # Pre-trained V-JEPA model
self.action_dim = action_dim
self.state_dim = state_dim
# Action conditioning network
self.action_encoder = nn.Sequential(
nn.Linear(action_dim, 256),
nn.ReLU(),
nn.Linear(256, state_dim)
)
# State transition predictor
self.transition_head = nn.Sequential(
nn.Linear(state_dim * 2, 512), # state + action
nn.ReLU(),
nn.Linear(512, state_dim)
)
# Freeze V-JEPA weights initially
for param in self.vjepa.parameters():
param.requires_grad = False
def forward(self, current_state, action_sequence, horizon=5):
"""
Predict future states given current state and action sequence
Args:
current_state: [B, state_dim] current visual state representation
action_sequence: [B, horizon, action_dim] planned actions
horizon: number of steps to predict
Returns:
predicted_states: [B, horizon, state_dim] predicted future states
confidence: [B, horizon] prediction confidence scores
"""
B = current_state.shape[0]
predicted_states = []
confidence_scores = []
# Start with current state
state = current_state
for t in range(horizon):
# Get action at time t
action = action_sequence[:, t] # [B, action_dim]
# Encode action
action_embed = self.action_encoder(action) # [B, state_dim]
# Predict next state
state_action = torch.cat([state, action_embed], dim=1)
next_state = self.transition_head(state_action)
# Estimate prediction confidence (based on state uncertainty)
with torch.no_grad():
# Use V-JEPA's internal uncertainty estimation
confidence = self.estimate_prediction_confidence(state, next_state)
predicted_states.append(next_state)
confidence_scores.append(confidence)
# Update state for next iteration
state = next_state
return torch.stack(predicted_states, dim=1), torch.stack(confidence_scores, dim=1)
def estimate_prediction_confidence(self, current_state, predicted_state):
"""
Estimate confidence in world model predictions
Higher confidence for states similar to training distribution
"""
# Simple confidence based on prediction magnitude
# In practice, this would use more sophisticated uncertainty estimation
confidence = 1.0 / (1.0 + torch.norm(predicted_state - current_state, dim=1))
return torch.clamp(confidence, 0.1, 1.0)
class VJEPAVLAController(nn.Module):
"""
Integrated controller combining V-JEPA world model with VLA policy
"""
def __init__(self, vla_model, vjepa_world_model, planning_horizon=5):
super().__init__()
self.vla = vla_model # Pre-trained VLA model
self.world_model = vjepa_world_model
self.planning_horizon = planning_horizon
# Model-predictive control parameters
self.mpc_weight = 0.3 # Weight for MPC component
self.vla_weight = 0.7 # Weight for VLA component
# Reward function for planning (learned or hand-crafted)
self.reward_function = self._build_reward_function()
def _build_reward_function(self):
"""
Build reward function for world model planning
In practice, this could be learned from human feedback
"""
return nn.Sequential(
nn.Linear(768 + 7, 256), # state + action
nn.ReLU(),
nn.Linear(256, 64),
nn.ReLU(),
nn.Linear(64, 1) # scalar reward
)
def forward(self, observation, instruction, current_state):
"""
Generate action using combined VLA + V-JEPA planning
Args:
observation: [B, H, W, C] visual observation
instruction: text instruction for the task
current_state: [B, state_dim] current state representation
Returns:
action: [B, action_dim] combined action output
planning_info: dict with planning details
"""
# Get VLA action
vla_action = self.vla(observation, instruction) # [B, action_dim]
# Perform model-predictive control using world model
mpc_action, planning_info = self.model_predictive_control(
current_state,
vla_action,
observation,
instruction
)
# Combine VLA and MPC actions
combined_action = (self.vla_weight * vla_action +
self.mpc_weight * mpc_action)
planning_info['vla_action'] = vla_action
planning_info['mpc_action'] = mpc_action
planning_info['combined_action'] = combined_action
return combined_action, planning_info
def model_predictive_control(self, current_state, vla_hint, observation, instruction):
"""
Model-predictive control using V-JEPA world model
Args:
current_state: current state representation
vla_hint: action suggestion from VLA model
observation: visual observation
instruction: task instruction
Returns:
best_action: [B, action_dim] optimal first action
planning_info: planning details and diagnostics
"""
B = current_state.shape[0]
device = current_state.device
# Generate candidate action sequences
num_candidates = 50 # Number of action sequences to evaluate
# Use VLA action as one candidate, add noise for exploration
candidate_sequences = []
# Add VLA-based sequence with small variations
for i in range(num_candidates // 2):
noise = torch.randn_like(vla_hint) * 0.1
vla_variant = vla_hint + noise
# Extend to full sequence (repeat with decay)
sequence = []
for t in range(self.planning_horizon):
decay = 0.9 ** t
sequence.append(vla_variant * decay)
candidate_sequences.append(torch.stack(sequence, dim=1))
# Add random exploration sequences
for i in range(num_candidates - num_candidates // 2):
random_sequence = torch.randn(B, self.planning_horizon, 7, device=device) * 0.2
candidate_sequences.append(random_sequence)
candidate_sequences = torch.stack(candidate_sequences, dim=1) # [B, num_candidates, horizon, action_dim]
# Evaluate each candidate sequence
best_rewards = -float('inf') * torch.ones(B, device=device)
best_actions = torch.zeros(B, 7, device=device)
best_sequences = torch.zeros(B, self.planning_horizon, 7, device=device)
for cand_idx in range(num_candidates):
sequence = candidate_sequences[:, cand_idx] # [B, horizon, action_dim]
# Predict future states using world model
predicted_states, confidence = self.world_model(current_state, sequence, self.planning_horizon)
# Compute cumulative reward for this sequence
total_reward = torch.zeros(B, device=device)
for t in range(self.planning_horizon):
# Reward for predicted state and action
state_action = torch.cat([predicted_states[:, t], sequence[:, t]], dim=1)
step_reward = self.reward_function(state_action).squeeze(-1)
# Weight by confidence and discount factor
discount = 0.95 ** t
total_reward += step_reward * confidence[:, t] * discount
# Update best sequence for each batch element
better_mask = total_reward > best_rewards
best_rewards[better_mask] = total_reward[better_mask]
best_actions[better_mask] = sequence[better_mask, 0] # First action
best_sequences[better_mask] = sequence[better_mask]
planning_info = {
'best_reward': best_rewards,
'best_sequence': best_sequences,
'num_candidates_evaluated': num_candidates,
'planning_horizon': self.planning_horizon
}
return best_actions, planning_info
# Training utilities for the integrated system
def train_vjepa_vla_system(vjepa_vla_controller, robot_data_loader, num_epochs=100):
"""
Train the integrated V-JEPA + VLA system on robot interaction data
"""
optimizer = torch.optim.Adam(vjepa_vla_controller.parameters(), lr=1e-4)
for epoch in range(num_epochs):
total_loss = 0.0
for batch in robot_data_loader:
observations = batch['observations'] # [B, T, H, W, C]
actions = batch['actions'] # [B, T, action_dim]
instructions = batch['instructions'] # List of text instructions
rewards = batch['rewards'] # [B, T]
batch_size, seq_length = observations.shape[:2]
# Process sequence step by step
for t in range(seq_length - 1):
current_obs = observations[:, t]
current_instruction = [inst for inst in instructions]
target_action = actions[:, t]
actual_reward = rewards[:, t]
# Get current state representation (from V-JEPA encoder)
with torch.no_grad():
current_state = vjepa_vla_controller.world_model.vjepa.context_encoder(
current_obs.unsqueeze(1) # Add time dimension
).mean(dim=1) # Pool over spatial dimensions
# Forward pass
predicted_action, planning_info = vjepa_vla_controller(
current_obs, current_instruction, current_state
)
# Compute losses
# 1. Action prediction loss
action_loss = F.mse_loss(predicted_action, target_action)
# 2. Reward prediction loss (if reward function is being learned)
predicted_reward = vjepa_vla_controller.reward_function(
torch.cat([current_state, predicted_action], dim=1)
).squeeze(-1)
reward_loss = F.mse_loss(predicted_reward, actual_reward)
# 3. World model consistency loss
# Predict next state and compare with actual next observation
next_obs = observations[:, t + 1]
with torch.no_grad():
next_state_actual = vjepa_vla_controller.world_model.vjepa.context_encoder(
next_obs.unsqueeze(1)
).mean(dim=1)
predicted_states, _ = vjepa_vla_controller.world_model(
current_state, predicted_action.unsqueeze(1), horizon=1
)
world_model_loss = F.mse_loss(predicted_states[:, 0], next_state_actual)
# Total loss
total_loss_step = action_loss + 0.1 * reward_loss + 0.05 * world_model_loss
total_loss += total_loss_step.item()
# Backward pass
optimizer.zero_grad()
total_loss_step.backward()
optimizer.step()
if epoch % 10 == 0:
avg_loss = total_loss / len(robot_data_loader)
print(f"Epoch {epoch}, Average Loss: {avg_loss:.4f}")
return vjepa_vla_controller
V-JEPA's power comes from learning rich world models from diverse video data. Unlike supervised approaches, V-JEPA learns through self-supervised prediction, making it scalable to internet-scale video datasets.
V-JEPA represents a crucial step toward AGI by demonstrating how AI systems can learn rich world models through self-supervision. These world models are essential for planning, reasoning, and understanding causalityโcore components of general intelligence.