视觉语言导航从入门到精通（四）：前沿方法与最新进展
本文是「视觉语言导航从入门到精通」系列的第四篇，介绍VLN领域的前沿方法和最新研究进展。
文章目录
- [1. VLN研究趋势概览](#1. VLN研究趋势概览)
- [2. 大语言模型驱动的VLN](#2. 大语言模型驱动的VLN)
- [3. 视觉语言模型端到端导航](#3. 视觉语言模型端到端导航)
- [4. 连续环境与真实世界部署](#4. 连续环境与真实世界部署)
- [5. 3D场景理解与空间推理](#5. 3D场景理解与空间推理)
- [6. 零样本与少样本VLN](#6. 零样本与少样本VLN)
- [7. 多模态预训练方法](#7. 多模态预训练方法)
- [8. 开放挑战与未来方向](#8. 开放挑战与未来方向)
1. VLN研究趋势概览
1.1 技术演进路线
VLN领域经历了三个主要发展阶段：
| 阶段 | 时间 | 代表方法 | 核心特点 |
|---|---|---|---|
| 第一阶段 | 2017-2019 | Seq2Seq, Speaker-Follower | RNN+Attention基础架构 |
| 第二阶段 | 2020-2022 | VLNBERT, HAMT, DUET | Transformer预训练范式 |
| 第三阶段 | 2023-至今 | NavGPT, VLM-Nav | LLM/VLM大模型时代 |
1.2 2023-2024年重要工作
顶会论文统计
| 会议 | 年份 | VLN相关论文数 | 代表工作 |
|---|---|---|---|
| CVPR | 2024 | 15+ | GridMM, BEVBert, VLN-SIG |
| NeurIPS | 2023 | 10+ | 3D-LLM, EmbodiedGPT |
| ICCV | 2023 | 12+ | VLN-PETL, NaviLLM |
| ECCV | 2024 | 8+ | MapGPT, SceneVLN |
2. 大语言模型驱动的VLN
2.1 LLM作为导航规划器
大语言模型在VLN中的典型应用范式：
架构设计
输入处理流程
| 输入 | 处理 | 输出 |
|---|---|---|
| 视觉观察 | 图像描述模型 | 场景文本描述 |
| 导航指令 | 直接输入 | - |
| 历史轨迹 | 轨迹编码 | 历史文本 |
场景文本 + 导航指令 + 历史文本 ──> LLM (GPT-4等) ──> 动作决策 + 推理解释2.2 NavGPT深入分析
python
import openai
from dataclasses import dataclass
from typing import List, Dict, Optional
@dataclass
class NavigationState:
"""导航状态封装"""
current_position: str
visible_objects: List[str]
scene_description: str
navigation_history: List[str]
available_actions: List[str]
class NavGPT:
"""
NavGPT: GPT驱动的视觉语言导航
参考: Zhou et al., "NavGPT: Explicit Reasoning in Vision-and-Language Navigation
with Large Language Models", arXiv 2023
"""
def __init__(self, model="gpt-4", temperature=0.2):
self.model = model
self.temperature = temperature
self.system_prompt = self._build_system_prompt()
def _build_system_prompt(self) -> str:
return """你是一个智能导航助手，需要根据自然语言指令在室内环境中导航。
你的任务是：
1. 理解用户的导航指令
2. 分析当前场景中的视觉信息
3. 结合历史轨迹做出合理的导航决策
4. 给出清晰的推理过程
在每一步，你需要输出：
- THOUGHT: 你的推理过程
- ACTION: 选择的动作（从可用动作列表中选择）
- REASON: 选择该动作的原因"""
def _format_observation(self, state: NavigationState) -> str:
"""格式化观察信息为文本"""
obs_text = f"""
当前场景描述: {state.scene_description}
可见物体: {', '.join(state.visible_objects)}
已走过的路径: {' -> '.join(state.navigation_history) if state.navigation_history else '起点'}
可选动作:
{chr(10).join([f'- {action}' for action in state.available_actions])}
"""
return obs_text
def navigate(self, instruction: str, state: NavigationState) -> Dict:
"""
执行一步导航决策
Args:
instruction: 导航指令
state: 当前导航状态
Returns:
包含action, thought, reason的字典
"""
observation = self._format_observation(state)
user_prompt = f"""
导航指令: {instruction}
当前观察:
{observation}
请分析当前情况并选择下一步动作。
"""
response = openai.ChatCompletion.create(
model=self.model,
messages=[
{"role": "system", "content": self.system_prompt},
{"role": "user", "content": user_prompt}
],
temperature=self.temperature
)
return self._parse_response(response.choices[0].message.content)
def _parse_response(self, response: str) -> Dict:
"""解析LLM响应"""
result = {
"thought": "",
"action": "",
"reason": ""
}
lines = response.strip().split('\n')
current_key = None
for line in lines:
line = line.strip()
if line.startswith("THOUGHT:"):
current_key = "thought"
result[current_key] = line[8:].strip()
elif line.startswith("ACTION:"):
current_key = "action"
result[current_key] = line[7:].strip()
elif line.startswith("REASON:"):
current_key = "reason"
result[current_key] = line[7:].strip()
elif current_key and line:
result[current_key] += " " + line
return result2.3 DiscussNav: 多Agent讨论式导航
python
class DiscussNav:
"""
多LLM讨论式导航决策
思路: 多个LLM角色从不同角度分析，投票决策
"""
def __init__(self):
self.agents = {
"explorer": self._create_explorer_agent(),
"landmark_tracker": self._create_landmark_agent(),
"instruction_follower": self._create_instruction_agent(),
"safety_checker": self._create_safety_agent()
}
def _create_explorer_agent(self):
"""探索者：倾向于探索新区域"""
return {
"role": "explorer",
"prompt": "你是一个探索者，倾向于选择能够探索更多新区域的动作。"
}
def _create_landmark_agent(self):
"""地标追踪者：关注指令中提到的物体"""
return {
"role": "landmark_tracker",
"prompt": "你关注指令中提到的地标和物体，选择能接近这些目标的动作。"
}
def _create_instruction_agent(self):
"""指令执行者：严格按指令执行"""
return {
"role": "instruction_follower",
"prompt": "你严格按照指令的字面意思执行，关注方向和顺序描述。"
}
def _create_safety_agent(self):
"""安全检查者：避免重复和死循环"""
return {
"role": "safety_checker",
"prompt": "你负责检查是否出现重复路径或死循环，必要时建议新方向。"
}
def discuss_and_decide(self, instruction: str, state: NavigationState) -> str:
"""
多Agent讨论并投票决策
"""
proposals = {}
# 每个Agent提出建议
for name, agent in self.agents.items():
proposal = self._get_agent_proposal(agent, instruction, state)
proposals[name] = proposal
# 投票机制
action_votes = {}
for name, proposal in proposals.items():
action = proposal["action"]
if action not in action_votes:
action_votes[action] = []
action_votes[action].append({
"agent": name,
"confidence": proposal.get("confidence", 0.5),
"reason": proposal["reason"]
})
# 加权投票选择最终动作
final_action = max(action_votes.keys(),
key=lambda a: sum(v["confidence"] for v in action_votes[a]))
return final_action2.4 LLM方法的性能对比
R2R val_unseen测试结果 (2023-2024)
| 方法 | SR | SPL | 特点 |
|---|---|---|---|
| HAMT (2021) | 66.0 | 61.0 | Transformer基线 |
| DUET (2022) | 69.0 | 59.0 | 双流架构 |
| NavGPT (2023) | 53.5 | 47.2 | 零样本，GPT-4 |
| NavGPT + Finetune | 67.2 | 58.1 | 微调后 |
| MapGPT (2024) | 70.1 | 62.3 | 地图增强 |
| NaviLLM (2024) | 71.8 | 63.5 | 多任务统一 |
3. 视觉语言模型端到端导航
3.1 VLM直接决策架构
python
import torch
import torch.nn as nn
from transformers import CLIPModel, CLIPProcessor
class VLMNavigator(nn.Module):
"""
基于视觉语言模型的端到端导航
直接从图像和指令预测动作
"""
def __init__(self, num_actions=4, hidden_dim=512):
super().__init__()
# 加载预训练VLM
self.clip = CLIPModel.from_pretrained("openai/clip-vit-large-patch14")
self.processor = CLIPProcessor.from_pretrained("openai/clip-vit-large-patch14")
clip_dim = self.clip.config.projection_dim # 768
# 多视角融合
self.view_attention = nn.MultiheadAttention(
embed_dim=clip_dim,
num_heads=8,
batch_first=True
)
# 时序建模
self.temporal_encoder = nn.TransformerEncoder(
nn.TransformerEncoderLayer(d_model=clip_dim, nhead=8, batch_first=True),
num_layers=2
)
# 动作预测头
self.action_head = nn.Sequential(
nn.Linear(clip_dim * 2, hidden_dim),
nn.ReLU(),
nn.Dropout(0.1),
nn.Linear(hidden_dim, num_actions)
)
def encode_panorama(self, images: torch.Tensor) -> torch.Tensor:
"""
编码全景图像
Args:
images: [batch, num_views, 3, H, W]
"""
batch_size, num_views = images.shape[:2]
# 展平处理
images_flat = images.view(-1, *images.shape[2:])
# CLIP视觉编码
with torch.no_grad():
visual_features = self.clip.get_image_features(pixel_values=images_flat)
# 恢复形状
visual_features = visual_features.view(batch_size, num_views, -1)
# 多视角注意力融合
fused_visual, _ = self.view_attention(
visual_features, visual_features, visual_features
)
return fused_visual
def encode_instruction(self, instruction: str) -> torch.Tensor:
"""编码语言指令"""
inputs = self.processor(text=instruction, return_tensors="pt", padding=True)
with torch.no_grad():
text_features = self.clip.get_text_features(**inputs)
return text_features
def forward(self, images, instruction, history_features=None):
"""
前向传播
Args:
images: [batch, num_views, 3, H, W] 当前全景图
instruction: 导航指令文本
history_features: [batch, hist_len, hidden] 历史特征
"""
# 编码当前观察
visual_features = self.encode_panorama(images)
text_features = self.encode_instruction(instruction)
# 全局视觉特征
global_visual = visual_features.mean(dim=1)
# 融合视觉和语言
fused = torch.cat([global_visual, text_features], dim=-1)
# 动作预测
action_logits = self.action_head(fused)
return action_logits3.2 RT-2风格的动作Token化
python
class ActionTokenizer:
"""
将连续动作空间离散化为token
参考RT-2的设计思路
"""
def __init__(self, num_bins=256):
self.num_bins = num_bins
# 动作维度定义
self.action_dims = {
'forward': (-1.0, 1.0), # 前进距离
'rotation': (-180.0, 180.0), # 旋转角度
'stop': (0, 1) # 停止概率
}
def discretize(self, continuous_action: dict) -> list:
"""将连续动作转换为离散token"""
tokens = []
for dim_name, (min_val, max_val) in self.action_dims.items():
value = continuous_action.get(dim_name, 0)
# 归一化到[0, 1]
normalized = (value - min_val) / (max_val - min_val)
# 离散化
token = int(normalized * (self.num_bins - 1))
token = max(0, min(self.num_bins - 1, token))
tokens.append(token)
return tokens
def undiscretize(self, tokens: list) -> dict:
"""将离散token转换回连续动作"""
action = {}
for i, (dim_name, (min_val, max_val)) in enumerate(self.action_dims.items()):
token = tokens[i]
normalized = token / (self.num_bins - 1)
value = normalized * (max_val - min_val) + min_val
action[dim_name] = value
return action
class RT2StyleVLN(nn.Module):
"""RT-2风格的VLN模型"""
def __init__(self, vlm_backbone, action_tokenizer):
super().__init__()
self.vlm = vlm_backbone
self.tokenizer = action_tokenizer
# 动作token嵌入
self.action_embed = nn.Embedding(
action_tokenizer.num_bins * 3, # 3个动作维度
self.vlm.config.hidden_size
)
# 动作预测头
self.action_head = nn.Linear(
self.vlm.config.hidden_size,
action_tokenizer.num_bins
)
def forward(self, images, instruction):
"""自回归预测动作token序列"""
# VLM编码
hidden = self.vlm(images, instruction)
# 预测3个动作token
action_tokens = []
for i in range(3):
logits = self.action_head(hidden)
token = logits.argmax(dim=-1)
action_tokens.append(token)
# 自回归：将预测的token作为下一步输入
token_embed = self.action_embed(token + i * self.tokenizer.num_bins)
hidden = hidden + token_embed
return action_tokens4. 连续环境与真实世界部署
4.1 从离散到连续导航
离散 vs 连续导航对比
| 特性 | 离散导航 (R2R) | 连续导航 (R2R-CE) |
|---|---|---|
| 动作空间 | 选择viewpoint | 前进/转向/停止 |
| 位置精度 | 预定义节点 | 任意位置 |
| 路径规划 | 图搜索 | 局部避障 |
| 真实性 | 较低 | 接近真实 |
| 难度 | 中等 | 高 |
4.2 连续环境导航实现
python
import habitat
import numpy as np
from habitat.config.default import get_config
class ContinuousVLNAgent:
"""连续环境VLN智能体"""
def __init__(self, model, config_path):
self.model = model
# Habitat配置
self.config = get_config(config_path)
self.env = habitat.Env(config=self.config)
# 动作映射
self.action_map = {
0: "STOP",
1: "MOVE_FORWARD",
2: "TURN_LEFT",
3: "TURN_RIGHT"
}
# 低级控制器参数
self.forward_step = 0.25 # 米
self.turn_angle = 15 # 度
def waypoint_to_actions(self, current_pos, target_pos, current_heading):
"""
将高层waypoint转换为低级动作序列
Args:
current_pos: (x, y, z) 当前位置
target_pos: (x, y, z) 目标位置
current_heading: 当前朝向（弧度）
"""
actions = []
# 计算目标方向
dx = target_pos[0] - current_pos[0]
dz = target_pos[2] - current_pos[2]
target_heading = np.arctan2(dx, -dz)
# 计算需要转向的角度
angle_diff = target_heading - current_heading
angle_diff = np.arctan2(np.sin(angle_diff), np.cos(angle_diff))
# 转向
turn_steps = int(abs(np.degrees(angle_diff)) / self.turn_angle)
turn_action = 2 if angle_diff > 0 else 3 # LEFT or RIGHT
actions.extend([turn_action] * turn_steps)
# 前进
distance = np.sqrt(dx**2 + dz**2)
forward_steps = int(distance / self.forward_step)
actions.extend([1] * forward_steps) # MOVE_FORWARD
return actions
def navigate(self, instruction: str, max_steps: int = 500):
"""
执行完整导航
"""
observations = self.env.reset()
trajectory = []
for step in range(max_steps):
# 获取RGB和深度图
rgb = observations['rgb']
depth = observations['depth']
# 模型预测
with torch.no_grad():
action_logits = self.model(rgb, instruction)
action = action_logits.argmax().item()
# 执行动作
observations = self.env.step(action)
trajectory.append({
'position': self.env.sim.get_agent_state().position,
'action': self.action_map[action]
})
# 检查是否停止
if action == 0: # STOP
break
# 检查是否碰撞
if self.env.episode_over:
break
return trajectory4.3 Sim-to-Real迁移
python
class DomainAdaptation:
"""
模拟器到真实世界的域适应
"""
def __init__(self):
# 视觉域适应
self.visual_adapter = VisualDomainAdapter()
# 动作校准
self.action_calibrator = ActionCalibrator()
def adapt_visual_input(self, real_image):
"""
将真实图像适应到模拟器风格
或者将模型适应到真实图像
"""
# 方法1: 风格迁移
adapted_image = self.visual_adapter.transfer_style(
real_image,
target_style='matterport'
)
# 方法2: 使用域无关特征
# domain_invariant_features = self.visual_adapter.extract_invariant(real_image)
return adapted_image
def calibrate_action(self, predicted_action, robot_config):
"""
根据具体机器人配置校准动作
"""
# 考虑机器人物理特性
calibrated = self.action_calibrator.calibrate(
action=predicted_action,
wheel_radius=robot_config['wheel_radius'],
wheel_base=robot_config['wheel_base'],
max_velocity=robot_config['max_velocity']
)
return calibrated
class VisualDomainAdapter(nn.Module):
"""视觉域适应网络"""
def __init__(self):
super().__init__()
# 编码器（共享）
self.encoder = nn.Sequential(
nn.Conv2d(3, 64, 4, 2, 1),
nn.ReLU(),
nn.Conv2d(64, 128, 4, 2, 1),
nn.ReLU(),
nn.Conv2d(128, 256, 4, 2, 1),
nn.ReLU()
)
# 域分类器（对抗训练）
self.domain_classifier = nn.Sequential(
nn.Linear(256 * 8 * 8, 1024),
nn.ReLU(),
nn.Linear(1024, 2) # sim vs real
)
# 解码器（重建）
self.decoder = nn.Sequential(
nn.ConvTranspose2d(256, 128, 4, 2, 1),
nn.ReLU(),
nn.ConvTranspose2d(128, 64, 4, 2, 1),
nn.ReLU(),
nn.ConvTranspose2d(64, 3, 4, 2, 1),
nn.Tanh()
)5. 3D场景理解与空间推理
5.1 3D-LLM架构
python
class ThreeDLLM(nn.Module):
"""
3D-LLM: 注入3D空间理解能力的语言模型
参考: Hong et al., "3D-LLM: Injecting the 3D World into Large Language Models"
"""
def __init__(self, llm_backbone, point_encoder):
super().__init__()
self.llm = llm_backbone # e.g., LLaMA
self.point_encoder = point_encoder
# 3D到文本空间的投影
self.projector = nn.Sequential(
nn.Linear(256, 2048),
nn.GELU(),
nn.Linear(2048, self.llm.config.hidden_size)
)
# 位置编码
self.pos_encoding = PositionalEncoding3D(256)
def encode_3d_scene(self, point_cloud, colors=None):
"""
编码3D点云场景
Args:
point_cloud: [N, 3] 点云坐标
colors: [N, 3] 点云颜色（可选）
"""
# 点云特征提取
if colors is not None:
point_features = torch.cat([point_cloud, colors], dim=-1)
else:
point_features = point_cloud
# 3D位置编码
pos_encoded = self.pos_encoding(point_cloud)
# 点云编码器
scene_features = self.point_encoder(point_features, pos_encoded)
# 投影到LLM空间
scene_tokens = self.projector(scene_features)
return scene_tokens
def forward(self, point_cloud, instruction, colors=None):
"""
Args:
point_cloud: [batch, N, 3]
instruction: 导航指令
colors: [batch, N, 3]
"""
# 编码3D场景
scene_tokens = self.encode_3d_scene(point_cloud, colors)
# 文本tokenize
text_tokens = self.llm.tokenize(instruction)
text_embeds = self.llm.embed_tokens(text_tokens)
# 拼接场景token和文本token
combined = torch.cat([scene_tokens, text_embeds], dim=1)
# LLM处理
outputs = self.llm(inputs_embeds=combined)
return outputs
class PositionalEncoding3D(nn.Module):
"""3D位置编码"""
def __init__(self, d_model):
super().__init__()
self.d_model = d_model
# 可学习的频率参数
self.freq = nn.Parameter(torch.randn(3, d_model // 6))
def forward(self, coords):
"""
Args:
coords: [batch, N, 3] xyz坐标
"""
batch, N, _ = coords.shape
# 傅里叶特征
features = []
for i in range(3):
coord = coords[:, :, i:i+1] # [batch, N, 1]
freq = self.freq[i] # [d_model // 6]
# sin和cos编码
sin_feat = torch.sin(coord * freq)
cos_feat = torch.cos(coord * freq)
features.extend([sin_feat, cos_feat])
pos_encoding = torch.cat(features, dim=-1)
return pos_encoding5.2 场景图导航
python
class SceneGraphNavigator:
"""
基于场景图的导航
构建环境的语义拓扑表示
"""
def __init__(self, object_detector, relationship_predictor):
self.detector = object_detector
self.rel_predictor = relationship_predictor
self.scene_graph = {}
def build_scene_graph(self, observations: list):
"""
从观察序列构建场景图
"""
nodes = [] # 物体节点
edges = [] # 关系边
for obs_id, obs in enumerate(observations):
# 检测物体
detections = self.detector(obs['rgb'])
for det in detections:
node = {
'id': f"{obs_id}_{det['class']}_{det['id']}",
'class': det['class'],
'bbox': det['bbox'],
'position': self._estimate_position(det, obs['depth']),
'observation_id': obs_id
}
nodes.append(node)
# 预测物体间关系
for i, node1 in enumerate(nodes):
for j, node2 in enumerate(nodes):
if i >= j:
continue
rel = self.rel_predictor(node1, node2)
if rel['confidence'] > 0.5:
edges.append({
'source': node1['id'],
'target': node2['id'],
'relation': rel['type']
})
self.scene_graph = {'nodes': nodes, 'edges': edges}
return self.scene_graph
def query_navigation_target(self, instruction: str):
"""
从指令中提取目标并在场景图中定位
"""
# 提取指令中的目标物体
target_objects = self._extract_targets(instruction)
# 在场景图中搜索
candidates = []
for target in target_objects:
for node in self.scene_graph['nodes']:
if self._match_object(node, target):
candidates.append(node)
return candidates
def plan_path(self, current_node_id: str, target_node_id: str):
"""
在场景图上规划路径
"""
# 构建邻接表
adj = {}
for edge in self.scene_graph['edges']:
src, tgt = edge['source'], edge['target']
if src not in adj:
adj[src] = []
if tgt not in adj:
adj[tgt] = []
adj[src].append(tgt)
adj[tgt].append(src)
# BFS搜索路径
from collections import deque
queue = deque([(current_node_id, [current_node_id])])
visited = {current_node_id}
while queue:
node, path = queue.popleft()
if node == target_node_id:
return path
for neighbor in adj.get(node, []):
if neighbor not in visited:
visited.add(neighbor)
queue.append((neighbor, path + [neighbor]))
return None # 无法到达6. 零样本与少样本VLN
6.1 零样本VLN能力
python
class ZeroShotVLN:
"""
零样本VLN：不需要VLN数据训练
利用预训练VLM的泛化能力
"""
def __init__(self, vlm_model):
self.vlm = vlm_model
def compute_action_scores(self, observation, instruction, candidate_views):
"""
计算每个候选视角与指令的匹配分数
核心思想：利用VLM的图文匹配能力
选择与指令最匹配的视角对应的动作
"""
scores = []
for view in candidate_views:
# 构建prompt
prompt = f"This image shows the direction to: {instruction}"
# 计算图文匹配分数
score = self.vlm.compute_similarity(view['image'], prompt)
scores.append(score)
# 选择最高分的视角
best_idx = np.argmax(scores)
return candidate_views[best_idx]['action'], scores
def navigate_with_vlm(self, instruction, env, max_steps=30):
"""
使用VLM进行零样本导航
"""
trajectory = []
for step in range(max_steps):
# 获取当前观察
obs = env.get_observation()
# 获取候选视角
candidates = env.get_candidate_views()
# 计算动作分数
action, scores = self.compute_action_scores(
obs, instruction, candidates
)
# 检查停止条件
stop_score = self._compute_stop_score(obs, instruction)
if stop_score > max(scores):
action = 'STOP'
# 执行动作
env.step(action)
trajectory.append(action)
if action == 'STOP':
break
return trajectory
def _compute_stop_score(self, observation, instruction):
"""计算停止的合理性分数"""
prompt = f"The navigation goal '{instruction}' has been reached in this scene."
return self.vlm.compute_similarity(observation, prompt)6.2 少样本学习与快速适应
python
class FewShotVLNAdapter:
"""
少样本VLN适应
使用少量示例快速适应新环境/新指令风格
"""
def __init__(self, base_model, adapter_dim=64):
self.base_model = base_model
# LoRA风格的适应层
self.adapters = nn.ModuleDict({
'visual': LoRAAdapter(base_model.visual_dim, adapter_dim),
'text': LoRAAdapter(base_model.text_dim, adapter_dim),
'fusion': LoRAAdapter(base_model.fusion_dim, adapter_dim)
})
def adapt(self, support_set, num_steps=100, lr=1e-4):
"""
使用支持集快速适应
Args:
support_set: [(instruction, trajectory, observations), ...]
"""
# 只训练adapter参数
adapter_params = []
for adapter in self.adapters.values():
adapter_params.extend(adapter.parameters())
optimizer = torch.optim.Adam(adapter_params, lr=lr)
for step in range(num_steps):
total_loss = 0
for instruction, trajectory, observations in support_set:
# 前向传播
loss = self._compute_adaptation_loss(
instruction, trajectory, observations
)
total_loss += loss
# 更新adapter
optimizer.zero_grad()
total_loss.backward()
optimizer.step()
return self
def _compute_adaptation_loss(self, instruction, trajectory, observations):
"""计算适应损失"""
total_loss = 0
hidden = None
for t, (obs, target_action) in enumerate(zip(observations, trajectory)):
# 带adapter的前向传播
visual_feat = self.base_model.encode_visual(obs)
visual_feat = visual_feat + self.adapters['visual'](visual_feat)
text_feat = self.base_model.encode_text(instruction)
text_feat = text_feat + self.adapters['text'](text_feat)
fused = self.base_model.fuse(visual_feat, text_feat)
fused = fused + self.adapters['fusion'](fused)
action_logits = self.base_model.predict_action(fused)
loss = F.cross_entropy(action_logits, target_action)
total_loss += loss
return total_loss / len(trajectory)
class LoRAAdapter(nn.Module):
"""LoRA适应层"""
def __init__(self, input_dim, adapter_dim, alpha=1.0):
super().__init__()
self.down_proj = nn.Linear(input_dim, adapter_dim, bias=False)
self.up_proj = nn.Linear(adapter_dim, input_dim, bias=False)
self.alpha = alpha
# 初始化
nn.init.kaiming_uniform_(self.down_proj.weight)
nn.init.zeros_(self.up_proj.weight)
def forward(self, x):
return self.alpha * self.up_proj(self.down_proj(x))7. 多模态预训练方法
7.1 VLN专用预训练
python
class VLNPretrainer:
"""
VLN专用预训练框架
设计针对导航任务的预训练目标
"""
def __init__(self, model):
self.model = model
# 预训练任务头
self.mlm_head = nn.Linear(model.hidden_dim, model.vocab_size)
self.mvm_head = nn.Linear(model.hidden_dim, model.visual_vocab_size)
self.sap_head = nn.Linear(model.hidden_dim, 1) # 单步动作预测
self.og_head = nn.Linear(model.hidden_dim, 1) # 目标定位
def pretrain_step(self, batch):
"""
一步预训练
"""
losses = {}
# 1. Masked Language Modeling (MLM)
mlm_loss = self._mlm_loss(batch)
losses['mlm'] = mlm_loss
# 2. Masked Vision Modeling (MVM)
mvm_loss = self._mvm_loss(batch)
losses['mvm'] = mvm_loss
# 3. Single-step Action Prediction (SAP)
sap_loss = self._sap_loss(batch)
losses['sap'] = sap_loss
# 4. Object Grounding (OG)
og_loss = self._og_loss(batch)
losses['og'] = og_loss
# 总损失
total_loss = sum(losses.values())
return total_loss, losses
def _mlm_loss(self, batch):
"""掩码语言建模"""
masked_ids, labels = self._mask_tokens(batch['input_ids'])
outputs = self.model(
input_ids=masked_ids,
visual_features=batch['visual_features']
)
text_hidden = outputs['text_hidden']
logits = self.mlm_head(text_hidden)
loss = F.cross_entropy(
logits.view(-1, logits.size(-1)),
labels.view(-1),
ignore_index=-100
)
return loss
def _sap_loss(self, batch):
"""单步动作预测"""
outputs = self.model(
input_ids=batch['input_ids'],
visual_features=batch['visual_features']
)
# 预测每个候选viewpoint的分数
candidate_features = batch['candidate_features']
scores = self.sap_head(
outputs['fused_hidden'].unsqueeze(1) * candidate_features
).squeeze(-1)
loss = F.cross_entropy(scores, batch['target_action'])
return loss
def _og_loss(self, batch):
"""目标定位：预测指令中物体在视觉中的位置"""
outputs = self.model(
input_ids=batch['input_ids'],
visual_features=batch['visual_features']
)
# 视觉token的定位分数
visual_hidden = outputs['visual_hidden']
scores = self.og_head(visual_hidden).squeeze(-1)
# 目标是物体位置的one-hot
loss = F.binary_cross_entropy_with_logits(
scores, batch['object_locations']
)
return loss7.2 大规模预训练数据构建
python
class VLNDataGenerator:
"""
自动生成VLN预训练数据
"""
def __init__(self, env, speaker_model, captioner):
self.env = env
self.speaker = speaker_model
self.captioner = captioner
def generate_pretraining_data(self, num_samples=100000):
"""
生成大规模预训练数据
"""
data = []
for _ in range(num_samples):
# 1. 随机采样路径
path = self.env.sample_random_path(
min_length=3,
max_length=10
)
# 2. 收集路径上的视觉观察
observations = []
for viewpoint in path:
self.env.teleport(viewpoint)
obs = self.env.get_observation()
observations.append(obs)
# 3. 使用Speaker生成指令
visual_features = self._extract_features(observations)
instruction = self.speaker.generate(visual_features)
# 4. 使用Captioner生成场景描述
scene_descriptions = []
for obs in observations:
caption = self.captioner(obs['rgb'])
scene_descriptions.append(caption)
# 5. 构建预训练样本
sample = {
'path': path,
'observations': observations,
'instruction': instruction,
'scene_descriptions': scene_descriptions,
'visual_features': visual_features
}
data.append(sample)
return data8. 开放挑战与未来方向
8.1 当前主要挑战
| 挑战 | 描述 | 可能方向 |
|---|---|---|
| Sim2Real Gap | 模拟器与真实世界差异 | 域适应、真实数据收集 |
| 长程推理 | 复杂指令的长序列执行 | 层次化规划、记忆机制 |
| 动态环境 | 移动物体和人 | 预测模型、反应式规划 |
| 多语言支持 | 非英语指令 | 多语言预训练、翻译 |
| 安全性 | 避免危险行为 | 约束学习、安全验证 |
8.2 未来研究方向
1. 世界模型与导航
python
class WorldModelNavigator:
"""
基于世界模型的导航
学习环境动态，进行想象规划
"""
def __init__(self, world_model, policy):
self.world_model = world_model # 预测下一状态
self.policy = policy
def imagine_trajectory(self, current_state, instruction, horizon=10):
"""
在想象中规划轨迹
"""
imagined_states = [current_state]
actions = []
state = current_state
for _ in range(horizon):
# 策略选择动作
action = self.policy(state, instruction)
actions.append(action)
# 世界模型预测下一状态
next_state = self.world_model.predict(state, action)
imagined_states.append(next_state)
state = next_state
# 检查是否到达目标
if self._is_goal_reached(state, instruction):
break
return actions, imagined_states2. 多机器人协作导航
python
class MultiAgentVLN:
"""
多智能体协作导航
"""
def __init__(self, agents, communication_module):
self.agents = agents
self.comm = communication_module
def collaborative_navigate(self, task):
"""
协作完成复杂导航任务
"""
# 任务分解
subtasks = self.decompose_task(task)
# 任务分配
assignments = self.assign_tasks(subtasks, self.agents)
while not self.task_completed(task):
# 智能体间通信
messages = {}
for agent_id, agent in self.agents.items():
msg = agent.generate_message()
messages[agent_id] = msg
# 广播消息
shared_info = self.comm.broadcast(messages)
# 各智能体根据共享信息行动
for agent_id, agent in self.agents.items():
agent.update(shared_info)
agent.step()3. 终身学习导航
python
class LifelongVLNAgent:
"""
终身学习VLN智能体
持续学习新环境和新任务
"""
def __init__(self, base_model):
self.model = base_model
self.memory = EpisodicMemory()
self.skill_library = {}
def learn_from_experience(self, experience):
"""
从经验中学习
"""
# 存储经验
self.memory.store(experience)
# 提取可复用技能
skill = self.extract_skill(experience)
if skill:
self.skill_library[skill['name']] = skill
# 定期巩固学习
if self.should_consolidate():
self.consolidate_knowledge()
def navigate_with_memory(self, instruction, env):
"""
利用记忆辅助导航
"""
# 检索相关记忆
relevant_memories = self.memory.retrieve(
query=instruction,
current_observation=env.get_observation()
)
# 查找可用技能
applicable_skills = self.find_applicable_skills(instruction)
# 结合记忆和技能进行导航
return self.navigate(instruction, env, relevant_memories, applicable_skills)总结
本文介绍了VLN领域的前沿方法和最新进展：
关键技术趋势
- LLM集成：利用大语言模型的推理能力进行导航规划
- VLM端到端：直接从视觉-语言输入预测动作
- 3D理解：深度融合3D场景信息
- 零样本能力：减少对标注数据的依赖
未来展望
- 更强的泛化能力（跨环境、跨指令）
- 真实世界部署（Sim2Real）
- 多模态、多任务统一
- 安全可靠的导航行为
参考文献
1\] Zhou X, et al. "NavGPT: Explicit Reasoning in Vision-and-Language Navigation with Large Language Models." *arXiv 2023*. \[2\] Hong Y, et al. "3D-LLM: Injecting the 3D World into Large Language Models." *NeurIPS 2023*. \[3\] Brohan A, et al. "RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control." *arXiv 2023*. \[4\] Driess D, et al. "PaLM-E: An Embodied Multimodal Language Model." *ICML 2023*. \[5\] Shah D, et al. "LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language, Vision, and Action." *CoRL 2023*. \[6\] Chen S, et al. "MapGPT: Map-Guided Prompting for Unified Vision-and-Language Navigation." *arXiv 2024*. *** ** * ** *** *上一篇：[视觉语言导航从入门到精通（三）：核心模型架构详解](./03_%E6%A0%B8%E5%BF%83%E6%A8%A1%E5%9E%8B%E6%9E%B6%E6%9E%84%E8%AF%A6%E8%A7%A3.md)* *下一篇：[视觉语言导航从入门到精通（五）：实战代码与项目实践](./05_%E5%AE%9E%E6%88%98%E4%BB%A3%E7%A0%81%E4%B8%8E%E9%A1%B9%E7%9B%AE%E5%AE%9E%E8%B7%B5.md)*