Imagine asking a robot: Hey, pick up the red cup from the kitchen and bring it here.
Seems simple, right? But for artificial intelligence, this involves understanding language, navigating space, recognizing objects, and even providing real-time feedback.
This was precisely the challenge in the Alexa Prize SimBot Challenge, where we built a conversational agent with advanced capabilities to understand instructions, move through its environment, interact with objects, and communicate with the user.
At Q2BSTUDIO, a company specialized in technology development and services, we work with these innovative trends to offer solutions based on artificial intelligence and machine learning. Below, we explain how we use BERT, reinforcement learning, and multimodal learning to make this technology work.
Language Understanding with BERT
Natural language can be ambiguous and complex. A user might say Go to the refrigerator or Find the refrigerator and open it, and the robot must be able to extract the meaning behind each command.
To address this, we used BERT to convert text instructions into structured commands that can be executed sequentially, allowing the robot to perform the requested tasks without error.
Key Results:
- 92% accuracy in mapping instructions to tasks.
- Ability to interpret variations in wording beyond predefined rules.
- Improved interpretation of environment-specific terms.
- Real-time processing (<100ms per query).
Navigation with Path Planning and Reinforcement Learning
After understanding an instruction, the robot must move to its destination. We implemented A* search algorithms for structured environments and reinforcement learning techniques for trajectories in dynamic spaces.
Key Results:
- The A* algorithm achieved efficient navigation in controlled environments.
- Reinforcement learning allowed real-time navigation adjustments.
- Travel time was reduced by 40% compared to traditional algorithms.
Object Recognition and Interaction
Once at its destination, the robot must identify and manipulate objects. To do this, we trained a model based on YOLOv8, specialized in real-time detection of items such as cups, doors, and appliances.
Key Results:
- Real-time detection at 30 FPS.
- 97% accuracy in identifying common objects.
- Ability to follow detailed instructions like Pick up the blue book.
Natural Language Feedback
Beyond executing tasks, the robot must provide clear responses to the user. For this, we used a GPT-based model to generate dynamic and fluid responses, aiming to improve human-machine interaction.
Key Results:
- Adaptive feedback that improved user experience.
- 98% of test users considered the responses natural.
- 35% increase in task completion rate.
Conclusion
The combination of advanced NLP, path planning, real-time object detection, and language generation has allowed us to create collaborative robots capable of performing tasks with precision, learning from the environment, and improving communication with users.
At Q2BSTUDIO, we apply these innovative technologies in customized solutions for various industries, from automation to enhancing user experience with advanced AI.
