Interactively Picking Real-World Objects with Unconstrained Spoken Language Instructions
Science & Technology
Introduction
In a recent paper, we present a pioneering robotic system designed to perform object-picking tasks leveraging spoken language instructions. The primary goal of our system is to understand and process a wide range of verbal commands, including those that feature complex or colloquial expressions, thus allowing for a more natural interaction between humans and robots.
System Overview
Our robotic system operates by interpreting spoken language instructions through a dynamic human-robot dialogue framework. The system processes RGB images as input, subsequently extracting the origins of each object present. This extraction is achieved through a Combination of Convolutional Neural Networks (CNNs) and multi-layer perceptrons, which together facilitate the extraction of a rich visual feature vector.
Conversely, the spoken text instruction is transformed into embedding vector representations. The system employs Long Short-Term Memory (LSTM) networks alongside multi-layer perceptrons to generate textual feature vectors. This dual processing allows for a comparative analysis of the visual and textual feature vectors, enabling the system to identify the best match for the spoken command.
Additionally, our framework includes a specialized pathway for predicting the destination box where the selected object should be moved. The robotic system then takes the predicted target object’s information along with the destination box input to execute the command effectively.
Interaction Demonstrations
The following examples illustrate the system's capabilities during interaction:
User: "Can you move the brown floppy thing to the bottom?"
Robot: "Sure, I'll work on that."User: "Can you move the tissue box to the left?"
Robot: "Sure, I'll work on that."User: "Can you move the white and blue box to the right?"
Robot: "Sure, I'll work on that."User: "Can you move the orange triangle thing to the top-left?"
Robot: "Sure, I'll work on that."User: "Can you move the sandal to the top right?"
Robot: "Sorry, which one do you mean, the red one?"
User: "Sure, I'll work on that."
This interaction exemplifies the system's adaptability in understanding and executing various spoken commands, enhancing the practicality of human-robot collaboration in real-world environments.
Keywords
- Robotic system
- Object-picking tasks
- Spoken language instructions
- Human-robot dialogue
- RGB image processing
- Convolutional Neural Networks (CNN)
- Multi-layer perceptron
- Long Short-Term Memory (LSTM)
- Visual feature vector
- Textual feature vector
FAQ
Q1: What is the main purpose of the robotic system presented in the paper?
A1: The primary purpose is to perform object-picking tasks based on spoken language instructions, allowing natural interaction between humans and robots.
Q2: How does the system understand spoken commands?
A2: The system processes RGB images and spoken text instructions, extracting visual and textual feature vectors to identify the best match for the given command.
Q3: Can the system handle colloquial expressions?
A3: Yes, the robotic system is designed to understand a wide range of spoken language, including complex or colloquial expressions.
Q4: What technologies are used in the system?
A4: The system utilizes Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and multi-layer perceptrons for processing visual and textual data.
Q5: How does the robot execute the movement of objects?
A5: After identifying the target object and its destination box, the robotic system takes this input and performs the movement task accordingly.