When humans and animals learn new behaviors, they often only need to observe once to grasp the skill. However, for robots, this process is far more complex. With the advancement of computer vision, modern technology enables robots to mimic human movements by analyzing body posture detection systems. But requiring a human to demonstrate every action is both time-consuming and inefficient. This paper introduces a novel approach: allowing robots to imitate and learn from a single video of a person.
Previous research has shown that robots can learn a variety of complex skills through observation, such as pouring water, playing table tennis, or opening drawers. However, the way robots learn is quite different from how humans do it. While humans can adapt and generalize based on limited observation, robots typically require specific demonstrations or remote control inputs. The question then arises: How can we make robots learn like humans, by observing third-party demonstrations?
There are two main challenges in learning from a single video. First, differences in appearance and body structure between the human demonstrator and the robot cause domain shifts, making it difficult to align the actions. Second, learning from raw visual data usually requires large amounts of training data, with deep learning models often needing hundreds of thousands of images. In this paper, we address these challenges using a meta-learning-based approach that works effectively even with just one demonstration.
In our preliminary work, we extended meta-learning algorithms to handle the transfer between human demonstrations and robotic execution. Meta-learning allows models to quickly adapt to new tasks by leveraging prior knowledge. One popular method, MAML (Model-Agnostic Meta-Learning), optimizes initial parameters so that the model can be fine-tuned efficiently for new tasks after meta-training.
To enable robots to imitate human actions from a single video, we treat the problem as a reasoning task where the goal is to infer the robot’s strategy by combining prior knowledge with minimal evidence. This requires rich visual and object understanding. Our testing method involves two stages: first, a meta-training phase where the model learns generalizable patterns from human motion data, and second, a fast adaptation phase where the model applies this knowledge to new tasks.
The algorithm used in the meta-object phase can be summarized as follows:
[Image: Let the robot imitate learning through a video of only one person]
After meta-training, the model uses the learned prior knowledge to infer strategies for new tasks by combining it with new human demonstrations. The process is illustrated as:
[Image: Let the robot imitate learning through a video of only one person]
For learning from videos, we introduced a time-adaptive target that captures relevant information such as human intent and object interactions. We used a convolutional network to process temporal sequences, resulting in improved performance.
Our network architecture maps RGB images to motion distributions. It starts with several convolutional layers, extracts 2D feature points, and connects them to the robot’s structure. These features are then passed through fully connected layers to generate motion commands.
In our experiments, we aimed to answer three key questions: Can our method allow robots to learn from a single video? Can it generalize across different viewpoints? And how does it differ from traditional meta-learning approaches?
We evaluated the importance of time-adaptive targets and tested the method on multiple platforms, including the PR2 and Sawyer robots. The results showed significant improvements in success rates compared to previous methods.
In the PR2 experiment, the robot successfully performed tasks such as placing, pushing, and picking up objects. The success rate was notably higher than previous approaches, and the error analysis confirmed the effectiveness of the method.
For the Sawyer experiment, we focused on its 7-degree-of-freedom arm. The use of time-adaptive targets increased the success rate by 14%, demonstrating the value of incorporating temporal information during learning.
Despite these promising results, there are still limitations. While the model can learn to manipulate new objects from a single video, it has not yet been proven capable of learning entirely new actions from scratch. Future work will focus on improving data efficiency and expanding the model's capabilities.
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