Li Feifei's team's new work: brain-controlled robots do housework, giving brain-computer interfaces the ability to learn with few samples

You can use your brain, never your hands.

In the future, you may be able to ask a robot to help you with housework just by thinking about it. The NOIR system recently proposed by the team of Wu Jiajun and Li Feifei of Stanford University allows users to control robots to complete daily tasks through non-invasive electroencephalography devices.

NOIR can decode your EEG signals into a robot skill library. It can now complete tasks such as cooking sukiyaki, ironing clothes, grating cheese, playing tic-tac-toe, and even petting a robot dog. This modular system has powerful learning capabilities and can handle complex and varied tasks in daily life.

The Brain and Robot Interface (BRI) is a masterpiece of human art, science and engineering. We have seen it in countless science fiction works and creative arts, such as "The Matrix" and "Avatar"; but truly realizing BRI is not easy and requires breakthrough scientific research to create a device that can perfectly coordinate with humans. functioning robotic system.

One key component for such a system is the ability of machines to communicate with humans. In the process of human-machine collaboration and robot learning, the ways humans communicate their intentions include actions, button presses, gaze, facial expressions, language, etc. Communicating directly with robots through neural signals is the most exciting but also most challenging prospect.

Recently, a multidisciplinary joint team led by Wu Jiajun and Li Feifei of Stanford University proposed a universal intelligent BRI system NOIR (Neural Signal Operated Intelligent Robots/Neural Signal Operated Intelligent Robots).

Paper address: https://openreview.net/pdf?id=eyykI3UIHa

Project website: https://noir-corl.github.io/

The system is based on non-invasive electroencephalography (EEG) technology. According to reports, the main principle based on this system is hierarchical shared autonomy, that is, humans define high-level goals, and robots achieve their goals by executing low-level movement instructions. The system incorporates new advances in neuroscience, robotics, and machine learning to achieve improvements over previous methods. The team summarizes the contributions made.

First of all, NOIR is versatile, can be used for diverse tasks, and is easy to use by different communities. Research shows that NOIR can complete up to 20 daily activities; in comparison, previous BRI systems were often designed for one or a few tasks, or were simply simulation systems. Additionally, the NOIR system can be used by the general population with minimal training.

Secondly, the I in NOIR means that the robot system is intelligent and has adaptive capabilities. The robot is equipped with a diverse repertoire of skills that allow it to perform low-level actions without intensive human supervision. Using parameterized skill primitives such as Pick (obj-A) or MoveTo (x,y), robots can naturally acquire, interpret, and execute human behavioral goals.

In addition, the NOIR system also has the ability to learn what humans want to achieve during the collaboration process. Research shows that by leveraging recent advances in underlying models, the system can adapt to even very limited data. This can significantly improve the efficiency of the system.

NOIR’s key technical contributions include a modular workflow for decoding neural signals to understand human intent. You know, decoding human intended goals from neural signals is extremely challenging. To do this, the team's approach is to break down human intention into three major components: the object to be manipulated (What), the way to interact with the object (How), and the location of the interaction (Where). Their research shows that these signals can be decoded from different types of neural data. These decomposed signals can naturally correspond to parameterized robot skills and can be effectively communicated to the robot.

Three human subjects successfully used the NOIR system in 20 home activities involving desktop or mobile operations (including making sukiyaki, ironing clothes, playing tic-tac-toe, petting a robot dog, etc.), That is, completing these tasks through their brain signals!

Experiments show that by using humans as teachers for few-shot robot learning, the efficiency of the NOIR system can be significantly improved. This method of using human brain signals to collaborate to build intelligent robotic systems has great potential to develop vital assistive technologies for people, especially those with disabilities, to improve their quality of life.

NOIR System

The challenges this research seeks to solve include: 1. How to build a universal BRI system suitable for various tasks? 2. How to decode relevant communication signals from the human brain? 3. How to improve the intelligence and adaptability of robots to achieve more efficient collaboration? Figure 2 gives an overview of the system.

In this system, humans, as planning agents, perceive, plan, and communicate behavioral goals to the robot; while the robot uses predefined primitive skills to achieve these goals.

To achieve the overall goal of creating a universal BRI system, these two designs need to be collaboratively integrated. To this end, the team proposed a new brain signal decoding workflow and equipped the robot with a set of parameterized original skill libraries. Finally, the team used few-sample imitation learning technology to give the robot more efficient learning capabilities.

Brain: Modular decoding workflow

As shown in Figure 3, human intention will be decomposed into three components: the object to be manipulated (What), the way to interact with the object (How), and the interaction Where.

Decoding specific user intentions from EEG signals is not easy, but it can be accomplished through steady-state visual evoked potentials (SSVEP) and motor imagery. Briefly, the process includes:

• Select an object with a Steady State Visual Evoked Potential (SSVEP)

• Select skills and parameters via Motor Imagery (MI)

• Select via muscle tightening to confirm or Interrupt

Robot: Parameterized primitive skills

Parameterized primitive skills can be combined and reused for different tasks to achieve complex and diverse operations. Furthermore, these skills are very intuitive to humans. Neither humans nor agents need to understand the control mechanisms of these skills, so people can implement these skills through any method as long as they are robust and adaptable to diverse tasks.

The team used two robots in the experiment: one is a Franka Emika Panda robotic arm for desktop operation tasks, and the other is a PAL Tiago robot for mobile operation tasks. The following table gives the primitive skills of these two robots.

Using Robot Learning for Efficient BRI

The modular decoding workflow and primitive skill library described above lay the foundation for NOIR. However, the efficiency of such systems can be improved further. The robot should be able to learn the user's items, skills, and parameter selection preferences during the collaboration process, so that in the future it can predict the goals the user wants to achieve, achieve better automation, and make decoding simpler and easier. Since the position, pose, arrangement, and instance of the items may differ each time it is executed, learning and generalization capabilities are required. Additionally, learning algorithms should be highly sample efficient because collecting human data is expensive.

The team adopted two methods for this: retrieval-based few-sample item and skill selection, and single-sample skill parameter learning.

Retrieval-based few-sample item and skill selection. This method can learn implicit representations of the observed states. Given a new observed state, it finds the most similar state and corresponding action in the hidden space. Figure 4 gives an overview of the method.

During mission execution, data points consisting of images and human-selected "item-skill" pairs are recorded. These images are first encoded by a pre-trained R3M model to extract features useful for robot manipulation tasks, and then passed through a number of trainable fully connected layers. These layers are trained using contrastive learning with a triplet loss, which encourages images with the same "item-skill" label to be closer together in the hidden space. The learned image embeddings and "item-skill" labels are stored in memory.

During testing, the model retrieves the nearest data point in the hidden space and then suggests the item-skill pair associated with that data point to the human.

Single sample skill parameter learning. Parameter selection requires extensive human involvement, as the process requires precise cursor operation through motor imagery (MI). To reduce human effort, the team proposed a learning algorithm that predicts parameters given an item-skill pair used as the starting point for cursor control. Assuming that the user has successfully located the precise key point of picking up a cup handle, does it need to specify this parameter again in the future? Recently, basic models such as DINOv2 have made a lot of progress, and the corresponding semantic key points can be found, eliminating the need to specify parameters again.

Compared with previous work, the new algorithm proposed here is single-sample and predicts specific 2D points rather than semantic fragments. As shown in Figure 4, given a training image (360 × 240) and parameter selection (x, y), the model predicts semantically corresponding points in different test images. Specifically, the team used the pre-trained DINOv2 model to obtain semantic features.

Experiments and results

missions. The tasks selected for the experiment come from the BEHAVIOR and Activities of Daily Living benchmarks, which can reflect human daily needs to a certain extent. Figure 1 shows the experimental tasks, which include 16 desktop tasks and 4 mobile operation tasks.

Examples of experimental processes for making sandwiches and caring for COVID-19 patients are shown below.

Experimental process. During the experiment, the user stayed in an isolated room, remained still, watched the robot on the screen, and relied solely on brain signals to communicate with the robot.

System performance. Table 1 summarizes the system performance under two metrics: the number of attempts before success and the time to complete the task upon success.

Despite the long span and difficulty of these tasks, NOIR achieved very encouraging results: on average, it only took 1.83 attempts to complete the tasks.

Decoding accuracy. The accuracy with which brain signals are decoded is a key to the success of the NOIR system. Table 2 summarizes the decoding accuracy at different stages. It can be seen that the CCA (canonical correlation analysis) based on SSVEP can achieve a high accuracy of 81.2%, which means that the item selection is generally accurate.

Item and skill selection results. So, can the newly proposed robot learning algorithm improve the efficiency of NOIR? The researchers first assessed item and skill selection learning. To do this, they collected an offline dataset for the MakePasta task, with 15 training samples for each item-skill pair. Given an image, when the correct item and skill are predicted simultaneously, the prediction is considered correct. The results are shown in Table 3.

A simple image classification model using ResNet can achieve an average accuracy of 0.31, while the new method based on pre-trained ResNet backbone network can achieve a significantly higher 0.73, which highlights the contrastive learning and retrieval-based The importance of learning.

Results of single-sample parameter learning. The researchers compared the new algorithm against multiple benchmarks based on pre-collected data sets. Table 4 gives the MSE values ​​of the predicted results.

They also demonstrated the effectiveness of the parameter learning algorithm in actual task execution on the SetTable task. Figure 5 shows the human effort saved in controlling cursor movement.

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