Language Models for Robotics
Overview
Language models in robotics enable natural communication between humans and robots, bridging the gap between high-level human instructions and low-level robot actions. In Vision-Language-Action (VLA) systems, language models process natural language commands and integrate them with visual perception to generate appropriate robot behaviors. This chapter explores the integration of large language models (LLMs) with robotic systems, focusing on NVIDIA's contributions and practical implementations for humanoid robots.
The integration of language understanding in robotics has evolved from simple keyword matching to sophisticated neural language processing. Modern language models can understand complex, multi-step instructions, resolve ambiguities, and even learn new concepts through interaction. For humanoid robots, language models enable intuitive command interfaces that allow non-expert users to control complex robotic behaviors through natural conversation.
Types of Language Models for Robotics
Large Language Models (LLMs)
Large Language Models have revolutionized natural language processing in robotics by providing:
- Context Understanding: Ability to understand commands in context
- Reasoning Capabilities: Logical reasoning about tasks and environments
- Few-Shot Learning: Learning new concepts from minimal examples
- Multimodal Integration: Combining text with visual and other modalities
Open-Source LLMs for Robotics
import transformers
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
class RobotLLM:
def __init__(self, model_name="microsoft/DialoGPT-medium"):
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
self.model = AutoModelForCausalLM.from_pretrained(model_name)
# Add padding token if not present
if self.tokenizer.pad_token is None:
self.tokenizer.pad_token = self.tokenizer.eos_token
def process_command(self, command, context=""):
"""Process a natural language command with context"""
# Combine context and command
full_input = f"{context}\nRobot Command: {command}"
# Tokenize input
inputs = self.tokenizer.encode(full_input, return_tensors='pt')
# Generate response
with torch.no_grad():
outputs = self.model.generate(
inputs,
max_length=inputs.shape[1] + 50,
num_return_sequences=1,
pad_token_id=self.tokenizer.eos_token_id,
do_sample=True,
temperature=0.7
)
# Decode response
response = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
# Extract the generated part
generated = response[len(full_input):].strip()
return generated
def extract_intent(self, command):
"""Extract the intent from a command"""
prompt = f"""
Command: "{command}"
Intent: Extract the main action and objects from this command.
Response should be in JSON format with keys: action, objects, location.
"""
inputs = self.tokenizer.encode(prompt, return_tensors='pt')
with torch.no_grad():
outputs = self.model.generate(
inputs,
max_length=inputs.shape[1] + 100,
num_return_sequences=1,
pad_token_id=self.tokenizer.eos_token_id,
do_sample=True,
temperature=0.3
)
response = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
return response
NVIDIA's Language Models
NVIDIA has developed specialized language models for robotics applications:
import torch
import torch.nn as nn
class NVIDIARobotLanguageModel(nn.Module):
def __init__(self, vocab_size, hidden_dim=768, num_layers=12):
super().__init__()
# Embedding layer
self.embedding = nn.Embedding(vocab_size, hidden_dim)
# Transformer layers for language understanding
self.transformer = nn.TransformerEncoder(
nn.TransformerEncoderLayer(
d_model=hidden_dim,
nhead=12,
dim_feedforward=hidden_dim * 4,
dropout=0.1
),
num_layers=num_layers
)
# Output layers for intent classification
self.intent_classifier = nn.Linear(hidden_dim, 50) # 50 common robot intents
self.action_generator = nn.Linear(hidden_dim, 100) # 100 action types
# Positional encoding
self.pos_encoder = nn.Embedding(512, hidden_dim) # Max sequence length
def forward(self, input_ids, attention_mask=None):
# Embedding
x = self.embedding(input_ids)
pos_ids = torch.arange(input_ids.size(1)).unsqueeze(0).to(input_ids.device)
x = x + self.pos_encoder(pos_ids)
# Apply transformer
x = self.transformer(x.transpose(0, 1)).transpose(0, 1)
# Global average pooling for classification
if attention_mask is not None:
x = x * attention_mask.unsqueeze(-1)
x = x.sum(dim=1) / attention_mask.sum(dim=1, keepdim=True)
else:
x = x.mean(dim=1)
# Generate outputs
intent_logits = self.intent_classifier(x)
action_logits = self.action_generator(x)
return intent_logits, action_logits
Task Planning Language Models
Specialized models for converting language to task plans:
class TaskPlanningLM:
def __init__(self):
# Load a pre-trained model for task planning
self.task_model = self.load_task_model()
def load_task_model(self):
"""Load a model specialized for task planning from language"""
# This would typically load a fine-tuned model
# For example, a model trained on robot command datasets
pass
def parse_command_to_plan(self, command):
"""Parse a natural language command into a task plan"""
# Example: "Go to the kitchen and bring me a cup"
# Should generate: [navigate_to_kitchen, find_cup, grasp_cup, return_to_user]
task_plan = {
"tasks": [],
"objects": [],
"locations": [],
"constraints": []
}
# Simple parsing logic (in practice, this would use a trained model)
if "go to" in command.lower():
location = self.extract_location(command)
task_plan["tasks"].append(f"navigate_to_{location}")
if "bring" in command.lower() or "get" in command.lower():
obj = self.extract_object(command)
task_plan["objects"].append(obj)
task_plan["tasks"].extend(["find_object", "grasp_object", "transport_object"])
return task_plan
def extract_location(self, command):
"""Extract location from command"""
# Simple keyword matching (in practice, use NER model)
locations = ["kitchen", "living room", "bedroom", "office", "dining room"]
for loc in locations:
if loc in command.lower():
return loc
return "unknown"
def extract_object(self, command):
"""Extract object from command"""
# Simple keyword matching (in practice, use NER model)
objects = ["cup", "book", "phone", "keys", "water", "food"]
for obj in objects:
if obj in command.lower():
return obj
return "unknown"
Natural Language Understanding for Robotics
Semantic Parsing
Semantic parsing converts natural language into structured representations:
import spacy
from typing import Dict, List, Tuple
class SemanticParser:
def __init__(self):
# Load spaCy model for English
try:
self.nlp = spacy.load("en_core_web_sm")
except OSError:
print("Please install spaCy English model: python -m spacy download en_core_web_sm")
self.nlp = None
def parse_command(self, command: str) -> Dict:
"""Parse a command into semantic components"""
if not self.nlp:
return {"error": "spaCy model not loaded"}
doc = self.nlp(command)
# Extract components
subject = self.extract_subject(doc)
action = self.extract_action(doc)
objects = self.extract_objects(doc)
locations = self.extract_locations(doc)
attributes = self.extract_attributes(doc)
return {
"subject": subject,
"action": action,
"objects": objects,
"locations": locations,
"attributes": attributes,
"dependencies": [(token.text, token.dep_, token.head.text) for token in doc]
}
def extract_subject(self, doc) -> str:
"""Extract the subject of the sentence"""
for token in doc:
if token.dep_ == "nsubj":
return token.text
return "robot" # Default subject
def extract_action(self, doc) -> str:
"""Extract the main action/verb"""
for token in doc:
if token.pos_ == "VERB":
return token.lemma_
return "unknown"
def extract_objects(self, doc) -> List[str]:
"""Extract direct objects"""
objects = []
for token in doc:
if token.dep_ == "dobj":
objects.append(token.text)
return objects
def extract_locations(self, doc) -> List[str]:
"""Extract location prepositional phrases"""
locations = []
for token in doc:
if token.dep_ == "prep" and token.text in ["to", "at", "in", "on"]:
pobj = [child for child in token.children if child.dep_ == "pobj"]
if pobj:
locations.append(pobj[0].text)
return locations
def extract_attributes(self, doc) -> Dict[str, str]:
"""Extract adjectives and other attributes"""
attributes = {}
for token in doc:
if token.pos_ == "ADJ":
# Find what the adjective modifies
for child in doc:
if child.head == token.head and child.dep_ == "nsubj":
attributes[child.text] = token.text
return attributes
Intent Recognition
Identifying the user's intent from their command:
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import Pipeline
import numpy as np
class IntentRecognizer:
def __init__(self):
# Define possible intents
self.intents = [
"navigation",
"object_manipulation",
"human_interaction",
"information_request",
"task_execution",
"emergency_stop"
]
# Training data (in practice, this would be much larger)
self.training_data = [
("go to the kitchen", "navigation"),
("move to the living room", "navigation"),
("navigate to the office", "navigation"),
("pick up the red cup", "object_manipulation"),
("grasp the pen", "object_manipulation"),
("take the book", "object_manipulation"),
("hello robot", "human_interaction"),
("how are you", "human_interaction"),
("what can you do", "information_request"),
("tell me about yourself", "information_request"),
("stop immediately", "emergency_stop"),
("emergency stop", "emergency_stop")
]
# Create and train the model
self.pipeline = Pipeline([
('tfidf', TfidfVectorizer()),
('classifier', MultinomialNB())
])
texts, labels = zip(*self.training_data)
self.pipeline.fit(texts, labels)
def recognize_intent(self, command: str) -> Dict:
"""Recognize the intent of a command"""
# Predict intent
predicted_intent = self.pipeline.predict([command])[0]
confidence = max(self.pipeline.predict_proba([command])[0])
return {
"intent": predicted_intent,
"confidence": float(confidence),
"all_intents": dict(zip(self.intents, self.pipeline.predict_proba([command])[0]))
}
def extract_entities(self, command: str) -> Dict:
"""Extract named entities from command"""
if not hasattr(self, 'nlp') or self.nlp is None:
try:
self.nlp = spacy.load("en_core_web_sm")
except OSError:
return {"error": "spaCy model not available"}
doc = self.nlp(command)
entities = {
"objects": [],
"locations": [],
"colors": [],
"quantities": []
}
for ent in doc.ents:
if ent.label_ in ["OBJECT", "PRODUCT"]:
entities["objects"].append(ent.text)
elif ent.label_ in ["GPE", "LOC", "FAC"]:
entities["locations"].append(ent.text)
# Extract colors and quantities using pattern matching
for token in doc:
if token.pos_ == "ADJ" and token.text in ["red", "blue", "green", "yellow", "black", "white"]:
entities["colors"].append(token.text)
elif token.pos_ == "NUM":
entities["quantities"].append(token.text)
return entities
Integration with ROS 2 and NVIDIA Isaac
ROS 2 Language Interface
Creating a ROS 2 node for language processing:
import rclpy
from rclpy.node import Node
from std_msgs.msg import String
from geometry_msgs.msg import Twist
from sensor_msgs.msg import Image
from cv_bridge import CvBridge
import json
class LanguageInterfaceNode(Node):
def __init__(self):
super().__init__('language_interface_node')
# Initialize components
self.intent_recognizer = IntentRecognizer()
self.semantic_parser = SemanticParser()
self.vision_processor = None # Will be set later
self.bridge = CvBridge()
# Publishers and subscribers
self.command_sub = self.create_subscription(
String,
'/robot/command',
self.command_callback,
10
)
self.action_pub = self.create_publisher(
String,
'/robot/action_plan',
10
)
self.status_pub = self.create_publisher(
String,
'/robot/status',
10
)
self.get_logger().info('Language Interface Node initialized')
def command_callback(self, msg):
"""Process incoming language commands"""
command = msg.data
# Recognize intent
intent_result = self.intent_recognizer.recognize_intent(command)
# Parse semantics
semantic_result = self.semantic_parser.parse_command(command)
# Combine results
processed_command = {
"original_command": command,
"intent": intent_result,
"semantic": semantic_result,
"timestamp": self.get_clock().now().to_msg()
}
# Generate action plan based on intent
action_plan = self.generate_action_plan(processed_command)
# Publish action plan
action_msg = String()
action_msg.data = json.dumps(action_plan)
self.action_pub.publish(action_msg)
# Publish status
status_msg = String()
status_msg.data = f"Processing command: {command}, Intent: {intent_result['intent']}"
self.status_pub.publish(status_msg)
self.get_logger().info(f'Processed command: {command}')
def generate_action_plan(self, parsed_command):
"""Generate an action plan from parsed command"""
intent = parsed_command["intent"]["intent"]
if intent == "navigation":
return self.generate_navigation_plan(parsed_command)
elif intent == "object_manipulation":
return self.generate_manipulation_plan(parsed_command)
elif intent == "human_interaction":
return self.generate_interaction_plan(parsed_command)
elif intent == "information_request":
return self.generate_info_plan(parsed_command)
elif intent == "emergency_stop":
return self.generate_emergency_plan(parsed_command)
else:
return {"error": "Unknown intent", "action": "idle"}
def generate_navigation_plan(self, parsed_command):
"""Generate navigation action plan"""
semantic = parsed_command["semantic"]
locations = semantic.get("locations", [])
if locations:
target_location = locations[0]
return {
"action": "navigate",
"target": target_location,
"plan": [
{"step": "localize", "description": "Determine current position"},
{"step": "plan_path", "description": f"Plan path to {target_location}"},
{"step": "execute_navigation", "description": f"Navigate to {target_location}"}
]
}
else:
return {"error": "No target location specified", "action": "request_clarification"}
def generate_manipulation_plan(self, parsed_command):
"""Generate manipulation action plan"""
semantic = parsed_command["semantic"]
objects = semantic.get("objects", [])
if objects:
target_object = objects[0]
return {
"action": "manipulate",
"target": target_object,
"plan": [
{"step": "localize_object", "description": f"Find {target_object}"},
{"step": "approach_object", "description": f"Move to {target_object}"},
{"step": "grasp_object", "description": f"Grasp {target_object}"},
{"step": "transport_object", "description": f"Transport {target_object}"}
]
}
else:
return {"error": "No target object specified", "action": "request_clarification"}
NVIDIA Isaac Language Integration
NVIDIA Isaac provides specialized tools for language integration:
# Example using Isaac ROS for language processing
import rclpy
from rclpy.node import Node
from nlp_msgs.msg import NlpCommand, NlpResponse
from geometry_msgs.msg import PoseStamped
from std_msgs.msg import String
import json
class IsaacLanguageNode(Node):
def __init__(self):
super().__init__('isaac_language_node')
# NVIDIA Isaac specific language processing
self.setup_isaac_language_pipeline()
# Subscriptions
self.nlp_command_sub = self.create_subscription(
NlpCommand,
'/nlp/command',
self.nlp_command_callback,
10
)
# Publishers
self.nlp_response_pub = self.create_publisher(
NlpResponse,
'/nlp/response',
10
)
self.behavior_command_pub = self.create_publisher(
String,
'/behavior/command',
10
)
def setup_isaac_language_pipeline(self):
"""Setup NVIDIA Isaac specific language processing pipeline"""
# This would integrate with NVIDIA's Isaac language models
# and multimodal processing capabilities
pass
def nlp_command_callback(self, msg):
"""Process NLP command from Isaac pipeline"""
command_text = msg.text
confidence = msg.confidence
if confidence > 0.7: # Only process confident commands
# Process the command using our language understanding
result = self.process_command(command_text)
# Create response
response = NlpResponse()
response.success = result["success"]
response.behavior_plan = json.dumps(result["plan"])
response.confidence = result.get("confidence", confidence)
# Publish response
self.nlp_response_pub.publish(response)
# Also publish to behavior system
behavior_msg = String()
behavior_msg.data = json.dumps(result["plan"])
self.behavior_command_pub.publish(behavior_msg)
def process_command(self, command):
"""Process command using integrated language understanding"""
# Use multiple language processing components
intent_result = self.intent_recognizer.recognize_intent(command)
semantic_result = self.semantic_parser.parse_command(command)
# Generate appropriate plan based on intent
plan = self.generate_plan_for_intent(intent_result["intent"], semantic_result)
return {
"success": True,
"plan": plan,
"intent": intent_result["intent"],
"confidence": intent_result["confidence"]
}
def generate_plan_for_intent(self, intent, semantic):
"""Generate execution plan for specific intent"""
# This would generate Isaac-specific behavior trees or action sequences
if intent == "navigation":
return self.create_navigation_plan(semantic)
elif intent == "manipulation":
return self.create_manipulation_plan(semantic)
# ... other intents
else:
return {"error": "Unsupported intent"}
Language Grounding in VLA Systems
Vision-Language Grounding
Connecting language to visual observations:
import torch
import torch.nn as nn
import clip # OpenAI CLIP model
class VisionLanguageGrounding(nn.Module):
def __init__(self):
super().__init__()
# Load CLIP model for vision-language grounding
self.clip_model, self.preprocess = clip.load("ViT-B/32")
# Additional components for grounding
self.grounding_head = nn.Linear(512, 100) # Map to object classes
self.spatial_attention = nn.MultiheadAttention(512, 8)
def forward(self, image, text):
# Encode image and text with CLIP
image_features = self.clip_model.encode_image(image)
text_features = self.clip_model.encode_text(clip.tokenize([text]))
# Normalize features
image_features = image_features / image_features.norm(dim=-1, keepdim=True)
text_features = text_features / text_features.norm(dim=-1, keepdim=True)
# Compute similarity
similarity = torch.matmul(image_features, text_features.t())
return similarity
def ground_language_to_objects(self, image, command):
"""Ground language command to specific objects in image"""
# Extract objects from command
semantic_result = self.semantic_parser.parse_command(command)
target_objects = semantic_result["objects"]
if not target_objects:
return None
# Process image to find objects
with torch.no_grad():
image_tensor = self.preprocess(image).unsqueeze(0)
# For each target object, compute similarity
object_similarities = []
for obj in target_objects:
text_input = clip.tokenize([f"a photo of {obj}"])
similarity = self(image_tensor, text_input)
object_similarities.append(similarity.item())
# Return the most similar object
best_match_idx = max(range(len(object_similarities)),
key=lambda i: object_similarities[i])
return {
"target_object": target_objects[best_match_idx],
"similarity": object_similarities[best_match_idx],
"all_similarities": list(zip(target_objects, object_similarities))
}
Referring Expression Comprehension
Understanding language that refers to specific objects in the scene:
class ReferringExpressionComprehension:
def __init__(self):
# Initialize with object detection and language models
self.object_detector = None # YOLO or similar
self.language_model = None # LLM for understanding references
def comprehend_referring_expression(self, image, expression):
"""Comprehend a referring expression in the context of an image"""
# Detect objects in the image
objects = self.object_detector.detect(image)
# Parse the referring expression
parsed_expr = self.parse_referring_expression(expression)
# Match expression to detected objects
target_object = self.match_expression_to_objects(
parsed_expr, objects, image
)
return target_object
def parse_referring_expression(self, expression):
"""Parse a referring expression to extract constraints"""
# Example: "the red cup on the table"
# Should extract: color=red, object=cup, location=on table
doc = self.nlp(expression)
constraints = {
"color": [],
"size": [],
"shape": [],
"location": [],
"spatial_relation": []
}
for token in doc:
# Extract color adjectives
if token.pos_ == "ADJ" and token.text in ["red", "blue", "green", "yellow",
"large", "small", "big", "little"]:
if token.text in ["red", "blue", "green", "yellow"]:
constraints["color"].append(token.text)
else:
constraints["size"].append(token.text)
# Extract spatial relations
if token.dep_ == "prep":
pobj = [child for child in token.children if child.dep_ == "pobj"]
if pobj:
constraints["spatial_relation"].append({
"relation": token.text,
"object": pobj[0].text
})
return constraints
def match_expression_to_objects(self, constraints, objects, image):
"""Match referring expression constraints to detected objects"""
candidates = []
for obj in objects:
score = 0
# Check color match
if constraints["color"]:
obj_color = self.extract_object_color(image, obj["bbox"])
if obj_color in constraints["color"]:
score += 1
# Check size constraints
if constraints["size"]:
obj_size = self.calculate_object_size(obj["bbox"])
# Compare with size constraints
score += self.match_size_constraint(obj_size, constraints["size"])
# Check spatial relations
if constraints["spatial_relation"]:
score += self.match_spatial_constraint(obj, objects, constraints["spatial_relation"])
candidates.append((obj, score))
# Return the best matching object
if candidates:
best_obj, best_score = max(candidates, key=lambda x: x[1])
return best_obj if best_score > 0 else None
return None
Multimodal Language Models
CLIP Integration
OpenAI's CLIP model for vision-language understanding:
import clip
import torch
import torch.nn as nn
from PIL import Image
class CLIPRobotInterface(nn.Module):
def __init__(self):
super().__init__()
# Load pre-trained CLIP model
self.clip_model, self.preprocess = clip.load("ViT-B/32")
# Additional layers for robot-specific tasks
self.action_head = nn.Linear(512, 64) # 64 possible robot actions
self.object_head = nn.Linear(512, 100) # 100 object classes
def encode_image_text(self, image, text):
"""Encode both image and text using CLIP"""
# Preprocess image
image_input = self.preprocess(image).unsqueeze(0)
# Tokenize text
text_input = clip.tokenize([text])
# Get embeddings
image_features = self.clip_model.encode_image(image_input)
text_features = self.clip_model.encode_text(text_input)
return image_features, text_features
def compute_similarity(self, image, texts):
"""Compute similarity between image and multiple text descriptions"""
image_input = self.preprocess(image).unsqueeze(0)
text_input = clip.tokenize(texts)
with torch.no_grad():
logits_per_image, logits_per_text = self.clip_model(image_input, text_input)
probs = logits_per_image.softmax(dim=-1).cpu().numpy()
return probs[0] # Return probabilities for the image
def interpret_command(self, image, command):
"""Interpret a command in the context of the current image"""
# Compute similarities with different action descriptions
action_descriptions = [
"robot moving forward",
"robot turning left",
"robot turning right",
"robot stopping",
"robot grasping object",
"robot releasing object",
"robot navigating to location"
]
# Add the command itself
all_texts = action_descriptions + [command]
similarities = self.compute_similarity(image, all_texts)
# Get the most similar action
action_probs = similarities[:len(action_descriptions)]
command_similarity = similarities[-1]
best_action_idx = action_probs.argmax()
best_action = action_descriptions[best_action_idx]
return {
"command_similarity": float(command_similarity),
"predicted_action": best_action,
"action_probabilities": list(zip(action_descriptions, action_probs)),
"confidence": float(action_probs[best_action_idx])
}
NVIDIA's Multimodal Models
NVIDIA's specialized multimodal models for robotics:
class NVIDIAMultimodalModel(nn.Module):
def __init__(self, vision_encoder, language_encoder):
super().__init__()
self.vision_encoder = vision_encoder
self.language_encoder = language_encoder
# Cross-modal attention mechanism
self.cross_attention = nn.MultiheadAttention(
embed_dim=768,
num_heads=12
)
# Fusion layers
self.fusion_layer = nn.Sequential(
nn.Linear(768 * 2, 1024),
nn.ReLU(),
nn.Dropout(0.1),
nn.Linear(1024, 512)
)
# Task-specific heads
self.action_head = nn.Linear(512, 7) # 7-DOF action for humanoid
self.navigation_head = nn.Linear(512, 3) # x, y, theta for navigation
def forward(self, images, text_tokens):
# Encode vision and language separately
vision_features = self.vision_encoder(images) # [batch, seq_len, dim]
language_features = self.language_encoder(text_tokens) # [batch, seq_len, dim]
# Cross-attention between vision and language
attended_vision, _ = self.cross_attention(
language_features.transpose(0, 1),
vision_features.transpose(0, 1),
vision_features.transpose(0, 1)
)
# Fuse features
fused_features = self.fusion_layer(
torch.cat([
attended_vision.transpose(0, 1).mean(dim=1), # Average pooled
language_features.mean(dim=1) # Average pooled language
], dim=1)
)
# Generate outputs
action_output = self.action_head(fused_features)
navigation_output = self.navigation_head(fused_features)
return {
"action": action_output,
"navigation": navigation_output,
"fused_features": fused_features
}
# Example usage with NVIDIA's model
def create_nvidia_vla_model():
"""Create a VLA model using NVIDIA's architecture"""
# This would use NVIDIA's specific vision and language encoders
# vision_encoder = nvidia_isaac.get_vision_encoder()
# language_encoder = nvidia_isaac.get_language_encoder()
# For demonstration, we'll use placeholder encoders
class DummyEncoder(nn.Module):
def __init__(self):
super().__init__()
self.linear = nn.Linear(512, 768)
def forward(self, x):
# Placeholder implementation
batch_size = x.shape[0] if len(x.shape) > 0 else 1
return torch.randn(batch_size, 10, 768) # [batch, seq_len, dim]
vision_encoder = DummyEncoder()
language_encoder = DummyEncoder()
return NVIDIAMultimodalModel(vision_encoder, language_encoder)
Safety and Robustness Considerations
Command Validation
Validating language commands for safety:
class CommandValidator:
def __init__(self):
# Define safe commands and dangerous command patterns
self.safe_commands = {
"navigation": ["go to", "move to", "navigate to", "walk to"],
"manipulation": ["pick up", "grasp", "take", "place", "put"],
"interaction": ["hello", "hi", "help", "stop", "wait"]
}
self.dangerous_patterns = [
"jump off",
"break",
"destroy",
"harm",
"damage"
]
# Define safe operational boundaries
self.operational_boundaries = {
"max_speed": 1.0, # m/s
"max_lift_height": 2.0, # meters
"safe_distance": 0.5 # meters from humans
}
def validate_command(self, command, context=None):
"""Validate a command for safety and feasibility"""
result = {
"is_safe": True,
"is_feasible": True,
"warnings": [],
"suggested_alternatives": []
}
# Check for dangerous patterns
command_lower = command.lower()
for pattern in self.dangerous_patterns:
if pattern in command_lower:
result["is_safe"] = False
result["warnings"].append(f"Command contains potentially dangerous pattern: '{pattern}'")
# Check command type and parameters
intent_result = self.recognize_intent(command)
if intent_result["intent"] == "navigation":
location = self.extract_location(command)
if location in ["edge", "cliff", "dangerous area"]:
result["is_safe"] = False
result["warnings"].append(f"Navigation to '{location}' may be unsafe")
elif intent_result["intent"] == "manipulation":
obj = self.extract_object(command)
# Check if object is too heavy or dangerous
if obj in ["glass", "sharp object", "hot item"]:
result["warnings"].append(f"Manipulating '{obj}' may require special care")
# Check for feasibility
if not self.is_command_feasible(command, context):
result["is_feasible"] = False
result["suggested_alternatives"].append(
"Please rephrase the command or specify a feasible alternative"
)
return result
def recognize_intent(self, command):
"""Simple intent recognition for validation"""
command_lower = command.lower()
for intent, patterns in self.safe_commands.items():
for pattern in patterns:
if pattern in command_lower:
return {"intent": intent, "pattern": pattern}
return {"intent": "unknown", "pattern": None}
def extract_location(self, command):
"""Extract location from command"""
# Simple extraction (in practice, use NER)
locations = ["kitchen", "bedroom", "office", "bathroom", "living room",
"garden", "balcony", "roof", "basement", "attic"]
for loc in locations:
if loc in command.lower():
return loc
return "unknown"
def extract_object(self, command):
"""Extract object from command"""
# Simple extraction (in practice, use NER)
objects = ["cup", "book", "phone", "keys", "glass", "sharp object", "hot item"]
for obj in objects:
if obj in command.lower():
return obj
return "unknown"
def is_command_feasible(self, command, context):
"""Check if command is physically feasible"""
# Check if robot has necessary capabilities
# Check environmental constraints
# Check current state and resources
# For now, assume most commands are feasible
# In practice, this would involve complex checks
return True
Robustness to Ambiguity
Handling ambiguous language commands:
class AmbiguityResolver:
def __init__(self):
self.context_buffer = [] # Store recent context
self.object_reference_resolver = None # For resolving "it", "this", etc.
def resolve_ambiguity(self, command, context=None):
"""Resolve ambiguities in language commands"""
# Parse the command
parsed = self.parse_command(command)
# Identify potential ambiguities
ambiguities = self.identify_ambiguities(parsed, context)
if not ambiguities:
return {"command": command, "resolved": True, "clarifications": []}
# Attempt to resolve ambiguities using context
resolved_command = self.resolve_with_context(command, ambiguities, context)
# If still ambiguous, suggest clarifications
if self.has_remaining_ambiguities(resolved_command):
clarifications = self.generate_clarifications(ambiguities)
return {
"command": resolved_command,
"resolved": False,
"clarifications": clarifications
}
return {
"command": resolved_command,
"resolved": True,
"clarifications": []
}
def identify_ambiguities(self, parsed_command, context):
"""Identify potential ambiguities in parsed command"""
ambiguities = []
# Check for pronouns without clear referents
if any(pronoun in parsed_command.lower() for pronoun in ["it", "this", "that", "them"]):
ambiguities.append({
"type": "pronoun_reference",
"word": "pronoun",
"description": "Pronoun without clear referent"
})
# Check for underspecified locations
locations = self.extract_locations(parsed_command)
if any(loc in ["there", "here", "over there"] for loc in locations):
ambiguities.append({
"type": "location_underspecification",
"word": "location",
"description": "Vague location reference"
})
# Check for underspecified objects
objects = self.extract_objects(parsed_command)
if any(obj in ["one", "it", "that"] for obj in objects):
ambiguities.append({
"type": "object_underspecification",
"word": "object",
"description": "Vague object reference"
})
return ambiguities
def resolve_with_context(self, command, ambiguities, context):
"""Attempt to resolve ambiguities using context"""
resolved_command = command
# Resolve pronouns using context
for ambiguity in ambiguities:
if ambiguity["type"] == "pronoun_reference":
referent = self.resolve_pronoun(command, context)
if referent:
resolved_command = resolved_command.replace("it", referent)
resolved_command = resolved_command.replace("this", referent)
resolved_command = resolved_command.replace("that", referent)
# Resolve locations using spatial context
for ambiguity in ambiguities:
if ambiguity["type"] == "location_underspecification":
specific_location = self.resolve_vague_location(command, context)
if specific_location:
resolved_command = resolved_command.replace("there", specific_location)
resolved_command = resolved_command.replace("here", specific_location)
return resolved_command
def resolve_pronoun(self, command, context):
"""Resolve pronoun to specific object"""
# This would use more sophisticated coreference resolution
# For now, return the most recently mentioned object
if context and "objects" in context:
if context["objects"]:
return context["objects"][-1] # Most recent object
return None
def resolve_vague_location(self, command, context):
"""Resolve vague location to specific location"""
# This would use spatial reasoning
# For now, return a default resolution
if context and "current_location" in context:
return context["current_location"]
return "current location"
def has_remaining_ambiguities(self, command):
"""Check if command still has unresolved ambiguities"""
# Simple check for remaining vague terms
vague_terms = ["it", "this", "that", "there", "here", "the one", "that one"]
return any(term in command.lower() for term in vague_terms)
def generate_clarifications(self, ambiguities):
"""Generate clarification questions for ambiguities"""
questions = []
for ambiguity in ambiguities:
if ambiguity["type"] == "pronoun_reference":
questions.append("Could you specify which object you're referring to?")
elif ambiguity["type"] == "location_underspecification":
questions.append("Could you specify the exact location?")
elif ambiguity["type"] == "object_underspecification":
questions.append("Could you specify which object you mean?")
return questions
def parse_command(self, command):
"""Parse command using NLP tools"""
if not hasattr(self, 'nlp') or self.nlp is None:
try:
import spacy
self.nlp = spacy.load("en_core_web_sm")
except:
return command # Return as-is if NLP tools not available
doc = self.nlp(command)
return doc.text
Summary
Language models are essential components of VLA systems, enabling natural human-robot interaction through natural language commands. This chapter covered various types of language models used in robotics, from general LLMs to specialized task planning models. We explored the integration of language understanding with ROS 2 and NVIDIA Isaac, and discussed important concepts like vision-language grounding and multimodal processing.
The implementation of language models in robotics requires careful consideration of safety, robustness, and ambiguity resolution. As these systems become more sophisticated, they will enable more intuitive and natural interaction between humans and robots, making robotic technology more accessible to non-expert users.
NVIDIA's contributions to language processing in robotics, through specialized models and hardware acceleration, are enabling more capable and responsive robotic systems. The combination of advanced language understanding with visual perception and action generation is creating truly intelligent robotic agents that can understand and execute complex, natural language commands in dynamic environments.