PERSONAL SMART ROBOT WITH MODIFIED PERSONAL LARGE LANGUAGE MODEL AND GAUSSIAN SPLATTING FOR IMAGING

Abstract

This invention presents a revolutionary personal assistant robot integrating advanced visual processing, conversational AI, and adaptive mobility. By leveraging a modified large language model (LLM) for nuanced interactions and Gaussian Splatting for superior image recognition, this robot enhances communication, navigational capability, and environmental awareness. It offers personalized support for home, healthcare, education, and office settings, adapting to user preferences through continuous learning. This invention aims to embed smart robotics seamlessly into daily life, enhancing convenience, productivity, and companionship.

Specification

Description:Field of Invention:
The invention falls within the field of robotics, artificial intelligence, and computer vision, specifically focusing on personal assistant robots with adaptive interaction, movement, and visual recognition capabilities.
Background of the Invention:
Personal assistant robots have gained popularity in various domains, but their interaction is often limited by processing and sensory abilities, affecting their practicality in dynamic environments. Traditional assistants lack sophisticated navigation and environmental awareness, making them ineffective in real-world situations requiring adaptability, learning, and high-accuracy recognition. This invention addresses these limitations, integrating advanced AI and visual processing to make robotic assistants more interactive, contextually aware, and capable of performing complex physical tasks.
Objective of the Invention:
To develop a personal robot assistant that can naturally interact with users using context-aware language processing.
To enable advanced movement capabilities for navigating diverse environments and performing physical tasks.
To enhance visual processing with high-accuracy imaging techniques, supporting complex object recognition and spatial awareness.
To create a robot capable of learning from interactions and adapting to users' preferences for a personalized experience.
To design variants of the robot tailored for specific settings like healthcare, education, and household environments.
Summary of the Invention:
The Personal Smart Robot integrates a modified LLM for conversational depth, allowing it to respond accurately to complex queries and engage in contextually aware interactions. The LLM is enhanced for sentiment analysis and user preference adaptation, creating a personalized communication experience.
The robot's movement system enables it to navigate a range of environments, from household floors to hospital corridors. Using a combination of sensors, it maps surroundings and detects obstacles, which is essential for autonomous navigation and physical task execution.
For visual processing, Gaussian Splatting enhances image fidelity, allowing the robot to interpret visual data precisely. This technique supports object recognition, spatial mapping, and interaction with objects, making it suitable for applications requiring high spatial awareness. The robot continuously learns from interactions, adapting to user habits and preferences over time.


Embodiments of the Invention:
Variants of the robot can be customized for specific environments, such as a healthcare model with medical monitoring sensors or an educational variant with enhanced interactive teaching features. Each embodiment would retain core functionalities, with additional components tailored to the respective application's unique requirements.
Detailed Description of the Invention:
The core design of the Personal Smart Robot provides a foundation that can be tailored to meet specific needs, from healthcare to educational applications. These embodiments involve structural and functional adjustments, such as adding medical sensors or enhancing interactive capabilities, to suit different settings.
In a healthcare environment, the robot can be adapted to monitor and assist patients. Equipped with additional medical sensors for monitoring vital signs like heart rate, blood pressure, and oxygen saturation, it supports healthcare professionals by alerting them to significant changes in patient conditions. For example, in a hospital, the robot can move through patient rooms, collect data, and relay it to the healthcare team. Its personalized responses also offer emotional support, comforting patients, and reducing loneliness, especially for those in long-term care.
In educational settings, the Personal Smart Robot can be modified to facilitate interactive teaching. Equipped with features like a built-in multimedia system and interactive screens, it can support teachers by demonstrating educational content, conducting quizzes, and adapting lessons based on student responses. This embodiment also leverages the LLM to understand and adjust to individual students' learning styles, providing tailored feedback, making learning more engaging, and fostering personalized education experiences.
While healthcare and education represent two primary focuses, the versatility of the Personal Smart Robot allows for other adaptations. A model for social companionship, for instance, could prioritize interactive engagement, detect changes in user behavior, and proactively suggest activities. Similarly, an environmental research variant might be equipped with sensors for atmospheric or soil analysis, aiding scientists in field research.
The Personal Smart Robot achieves its high level of functionality and adaptability through a carefully engineered combination of three core systems: LLM integration, an advanced movement system, and high-fidelity visual processing.
LLM Integration: Human-Like Interaction and Personalization
The modified Large Language Model (LLM) is a cornerstone of the robot's interactive capabilities. Unlike traditional conversational systems, this LLM is adapted to process nuanced conversational contexts, engage in meaningful dialogue, and continually improve its responses based on ongoing user interaction.
Conversational Understanding and Response Generation
The LLM interprets both direct commands and subtle contextual cues, allowing the robot to provide responses that feel relevant and personalized. It can follow extended conversations, maintaining context, and delivering replies that consider previous user statements. This functionality is especially valuable in social settings, where the robot acts as a supportive companion, recalling past conversations and adjusting its communication style to the user's preferences.
Sentiment Analysis and Emotional Adaptation
Through integrated sentiment analysis, the LLM can assess the user's tone, word choice, and speech patterns to infer their emotional state. For instance, if a user sounds distressed, the robot responds with empathy, offering comfort or practical advice. This capability is critical in healthcare and social settings, where emotional sensitivity can enhance the user experience.
Learning and Adaptation to User Preferences
Over time, the LLM adjusts to user preferences, building a personalized interaction history that enables tailored responses. This adaptability means that frequent users experience more intuitive interactions, as the robot's understanding of individual needs deepens. For instance, a user might express a preference for specific daily reminders, which the robot integrates into its routines to provide a smoother, more intuitive experience.
Movement System: Adaptive Locomotion and Navigation
A sophisticated movement system gives the robot physical autonomy, allowing it to navigate various terrains and maneuver around obstacles. This functionality enables the robot to serve in dynamic environments, from crowded hospital wards to busy classrooms.
Locomotion System and Terrain Adaptability
The robot's locomotion system is designed to handle a variety of surfaces, including smooth indoor floors, carpeted areas, and outdoor terrains. Advanced wheels and jointed movement modules allow the robot to tackle mild inclines and adapt to uneven surfaces. This adaptability is essential for applications that require the robot to move seamlessly across different spaces.
Environmental Mapping and Path Planning
The movement system is equipped with sensors that constantly map the robot's surroundings. Using simultaneous localization and mapping (SLAM) techniques, it builds a 3D model of the environment, identifying obstacles, pathways, and important features. This spatial awareness enables the robot to navigate even complex environments autonomously, calculating optimal routes to avoid obstructions.
Obstacle Detection and Safety Features
Safety is a priority, especially in settings with high traffic or confined spaces. Proximity sensors and cameras continuously monitor for nearby objects, allowing the robot to stop, slow down, or change direction when an obstacle is detected. This real-time detection minimizes the risk of accidents and ensures that the robot can operate safely in crowded spaces or around sensitive equipment.
Visual Processing: Gaussian Splatting for Enhanced Perception
Visual processing is a crucial aspect of the robot's capability to interpret and interact with its surroundings. The use of Gaussian Splatting enables the robot to achieve high-fidelity image interpretation, enhancing object recognition, spatial awareness, and interaction with users.
High-Fidelity Visual Interpretation
Gaussian Splatting allows the robot to achieve detailed and accurate image reconstruction, improving its ability to interpret objects, faces, and gestures. This high level of detail is particularly beneficial in healthcare settings, where recognizing specific items or reading facial expressions can be critical for effective care.
Object Recognition and Spatial Awareness
The visual processing system, combined with Gaussian Splatting, enables the robot to recognize common objects, differentiate between individuals, and respond to environmental cues. In educational applications, for example, the robot can identify specific tools or educational materials and interact with them appropriately, creating a more interactive learning experience.
Enhanced Human Interaction through Gesture and Expression Recognition
Recognizing human gestures and facial expressions allows the robot to engage more naturally with users. By interpreting body language and facial cues, it can respond in ways that feel intuitive and empathetic. For instance, if a user smiles, the robot might respond with a friendly greeting, while a frown could prompt it to ask if assistance is needed.
Applications:
1. Home Environment: Manages smart devices, supports educational needs, and assists with daily routines.
2. Healthcare Facilities: Provides patient care assistance, monitors conditions, and offers companionship.
3. Educational Institutions: Acts as a teaching assistant, delivering personalized learning content and adapting to different learning styles.
4. Office Settings: Supports administrative tasks, scheduling, and information management.
Python Program:
import random
import time
class PersonalSmartRobot:
def __init__(self, name):
self.name = name
self.memory = []
def respond(self, user_input):
response = self.llm_response(user_input)
self.memory.append(user_input)
self.memory.append(response)
return response
def llm_response(self, user_input):
# Simulating sentiment analysis and personalized response
sentiments = ["happy", "sad", "angry", "neutral"]
sentiment = random.choice(sentiments)
if sentiment == "happy":
return f"{self.name}: I'm glad to hear that! How can I assist you today?"
elif sentiment == "sad":
return f"{self.name}: I'm sorry to hear that. If you want to talk, I'm here."
elif sentiment == "angry":
return f"{self.name}: It seems you're upset. How can I help resolve this?"
else:
return f"{self.name}: How can I assist you today?"
def move(self, destination):
print(f"{self.name} is navigating to {destination}...")
time.sleep(2) # Simulating movement delay
print(f"{self.name} has arrived at {destination}.")
def detect_objects(self, environment):
# Simulated object recognition
recognized_objects = []
for item in environment:
if random.random() > 0.5: # Simulating a 50% chance of recognizing an object recognized_objects.append(item)
return recognized_objects
# Example Usage
def main():
robot = PersonalSmartRobot("RoboAssistant")
# Simulated conversation
user_inputs = [
"I'm feeling great today!",
"I'm really sad about my friend.",
"I don't know what to do.",
"I need some help with my homework."
]
for user_input in user_inputs:
print(f"User: {user_input}")
response = robot.respond(user_input)
print(response)
# Simulate movement
robot.move("the living room")
robot.move("the kitchen")
# Simulate object detection in the environment
environment = ["chair", "table", "book", "computer", "plant"]
recognized_objects = robot.detect_objects(environment)
print(f"Recognized Objects: {recognized_objects}")
if __name__ == "__main__":
main()
Explanation of the Program
Class Definition (PersonalSmartRobot):
The class PersonalSmartRobot encapsulates the main functionalities of the robot.
It has attributes like name and memory to keep track of conversations.
Respond Method:
The respond method takes user input and generates a response using a simulated LLM function.
LLM Response Simulation:
The llm_response method simulates sentiment analysis by randomly selecting a sentiment and crafting a relevant response.
Movement Simulation:
The move method simulates navigating to a destination with a delay to mimic movement.
Object Detection Simulation:
The detect_objects method simulates recognizing objects in the environment with a random chance of detection.
Example Usage:
In the main function, the robot engages in a simulated conversation, moves to different locations, and recognizes objects in a predefined environment.
, Claims:We Claim:
1. A personal smart robot comprising a modified large language model (LLM) for natural language processing and interaction, a movement system for autonomous navigation, and a Gaussian Splatting-based visual processing system, wherein the robot is capable of adapting to user preferences through machine learning, enabling it to perform personalized assistance tasks in various environments.
2. The robot of claim 1, wherein the LLM is configured for sentiment analysis to enhance user interaction.
3. The robot of claim 1, further comprising a mobility system capable of navigating uneven terrains.
4. The robot of claim 1, wherein Gaussian Splatting enables real-time high-fidelity image recognition for object detection.
5. The robot of claim 1, wherein the movement system includes obstacle detection sensors for safe navigation.
6. The robot of claim 1, further configured to manage smart home devices autonomously.
7. The robot of claim 1, adapted for healthcare environments, including patient monitoring sensors.
8. The robot of claim 1, wherein the LLM is customized for contextual learning, adjusting responses based on user interactions.
9. The robot of claim 1, wherein visual processing supports facial recognition for personalized responses.
10. The robot of claim 1, adapted for educational environments with interactive teaching modules.

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