教機器人透過語言溝通學習新技能

Table of Contents:

1. 🤖 Introduction
2. 🎯 Language Communication in Human Learning
3. 💡 Teaching Robots New Skills Through Language Communication
4. 🤔 Challenges in Teaching Robots through Language
5. 🔎 Grounding Verb Arguments to Perception
6. 🖼️ Incorporating Visual Cues to Improve Grounding
7. 🤝 Linking Verb Representations with Planning Systems
8. 📺 Demo: Teaching Baxter Robot to Make Smoothies
9. 🔀 Naive Physical Action Prediction to Enhance Robot Learning
10. 🌐 Web Data and Bootstrapping to Handle Noise
11. 📚 Conclusion: Collaborative Effort in Enabling Language Communication with Robots

🤖 Introduction

As a new member of the AI lab, I am thrilled to witness the exciting activities happening here, including today's symposium. Language communication has always played a vital role in human learning, as it facilitates the transfer of knowledge from teachers to students. However, the process of teaching robots new skills through language communication poses numerous challenges. In this article, we will explore the possibilities and obstacles in this field and discuss various approaches to overcome them. From grounding verb arguments to perception to incorporating visual cues and linking verb representations with planning systems, we will delve into the complexities of teaching robots through language. Let's embark on this fascinating journey together!

🎯 Language Communication in Human Learning

Language communication has long been recognized as a crucial factor in human learning. It allows teachers to convey knowledge to students in a selective and efficient manner, saving them from the process of trial and error. The effectiveness of this approach heavily relies on the communication and motivation of human teachers. Therefore, the question arises: Can we extend this mode of teaching to robots? Is it possible to teach robots new skills or knowledge through language communication?

💡 Teaching Robots New Skills Through Language Communication

Imagine having a robot that can learn to make your favorite smoothie. Teaching a robot such a skill involves providing it with language instructions and demonstrating the necessary actions. For instance, you can instruct the robot to pick up a strawberry, place it on the cutting board, and slice it into two pieces. Throughout this interaction, the robot can ask questions for clarification. The ultimate goal is for the robot to acquire a grounded task structure that encompasses hierarchical relations between tasks and subtasks, with linguistic symbols grounded in the physical world. This structure enables the robot to perceive and act accordingly in the future. However, arriving at this task structure is not a trivial task due to the inherent challenges.

🤔 Challenges in Teaching Robots through Language

Teaching robots new skills through language communication presents several challenges. Firstly, humans and robots exist in a shared world but possess mismatched capabilities. Consequently, their representations of the world differ significantly, making it difficult for the robot to follow language instructions seamlessly. Moreover, humans possess vast knowledge about how the world works, which the robot lacks. The reliance on concrete action verbs in language instructions further complicates the process. In the following sections, we will address specific problems related to grounding verb arguments to perception and linking verb representations with planning systems.

🔎 Grounding Verb Arguments to Perception

The semantics of verbs can be captured through frames, which specify the key ingredients and semantic rules for understanding different situations. To enable a robot to follow instructions, it must first identify Relevant frames from the given text and ground them to the physical environment. This becomes a challenging task, as state-of-the-art computer vision models struggle in unconstrained environments. Although annotated vision data can yield decent performance, automated vision falls short due to the persisting challenges in computer vision. Therefore, we need to explore language-side solutions to aid in grounding verbs.

🖼️ Incorporating Visual Cues to Improve Grounding

In the physical world, actions often entail changes in various Dimensions, which can be perceived from the environment. Studies have identified 18 dimensions of state change that can be observed and linked to the changed state caused by specific action verbs. Incorporating this causality knowledge into graphical models can significantly improve performance. By providing higher-level guidance for visual processing, such grounded verb semantics can aid robots in perceiving and interpreting language instructions more effectively.

🤝 Linking Verb Representations with Planning Systems

Linking verb representations with underlying planning systems is crucial for robots to perform instructed actions. However, robotic arms often have limited primitive actions, such as open gripper, close gripper, and move to. Consequently, translating complex actions, like putting an apple on a plate, into sequences of primitive actions becomes a planning challenge. To address this, we need to establish a connection between the verb representation of an action and the underlying planning system. By employing classical AI approaches and a hypothesis space for grounded verb semantics, we can incrementally update the hypothesis through continuous interaction with humans. This enables the robot to find the best hypothesis from its Knowledge Base for planning. Additionally, handling the uncertainties in the world becomes crucial, and reinforcement learning-based question answering strategies can effectively reduce uncertainty and improve the learning and execution phases.

📺 Demo: Teaching Baxter Robot to Make Smoothies

To demonstrate the application of language communication in teaching robots, we have implemented a system in our Baxter robot. This system allows the robot to learn how to make smoothies through step-by-step instructions. The robot can either perform the actions independently or observe a human executing them. By explicitly modeling the change of state as part of a well-grounded verb representation, we facilitate the robot's understanding of the task. We have also incorporated strategies to handle uncertainties by applying reinforcement learning to learn a policy for question answering. This not only improves the performance of the system but also reduces the number of turns required during the learning or execution process. A quick demo of the system showcases the robot's ability to learn and perform the instructions accurately.

🔀 Naive Physical Action Prediction to Enhance Robot Learning

To enhance the robot's learning capabilities, we explore the concept of naive physical action prediction. By providing a set of images describing different states of an object, such as a squeezed bottle, we aim to identify the states that describe the effect of a specific action. This allows the robot to learn from a small number of examples, enabling humans to teach the robot in real-time communication. Web data is utilized to supplement the learning process, utilizing a bootstrapping approach to handle noise in the data. While the performance in this area is currently poor, there is ample room for improvement. Incorporating such models into interactive task learning can be highly beneficial, as robots often encounter new action-verb pairs requiring Prompt learning through communication.

🌐 Web Data and Bootstrapping to Handle Noise

The integration of web data into robot learning processes can help supplement the limited data available. Through a bootstrapping approach, noisy web data can be utilized effectively. Novel algorithms and strategies are employed to handle uncertainty and noise, allowing the robot to handle real-world scenarios successfully. By continuously updating and refining the hypothesis space, robots can improve their performance and learn to adapt in complex and uncertain environments.

📚 Conclusion: Collaborative Effort in Enabling Language Communication with Robots

Enabling language communication with robots requires a dedicated and collaborative effort across multiple disciplines. As we navigate the landscape of teaching robots through language, it is essential to identify junctions and build pathways to enhance their learning capabilities. Drawing inspiration from child language acquisition and incorporating causal perception, we can improve the models and techniques used in teaching robots. The possibilities are vast, ranging from applications in Healthcare, education, and defense to assistive technologies and beyond. By embracing this exciting journey together, we can Shape the future of language communication with robots.

Resources:

• Baxter Robot
• Joint Attention in Child Development
• Causal Perception and Language Acquisition