One-Shot Imitation Learning (1703.07326v3)

Abstract: Imitation learning has been commonly applied to solve different tasks in isolation. This usually requires either careful feature engineering, or a significant number of samples. This is far from what we desire: ideally, robots should be able to learn from very few demonstrations of any given task, and instantly generalize to new situations of the same task, without requiring task-specific engineering. In this paper, we propose a meta-learning framework for achieving such capability, which we call one-shot imitation learning. Specifically, we consider the setting where there is a very large set of tasks, and each task has many instantiations. For example, a task could be to stack all blocks on a table into a single tower, another task could be to place all blocks on a table into two-block towers, etc. In each case, different instances of the task would consist of different sets of blocks with different initial states. At training time, our algorithm is presented with pairs of demonstrations for a subset of all tasks. A neural net is trained that takes as input one demonstration and the current state (which initially is the initial state of the other demonstration of the pair), and outputs an action with the goal that the resulting sequence of states and actions matches as closely as possible with the second demonstration. At test time, a demonstration of a single instance of a new task is presented, and the neural net is expected to perform well on new instances of this new task. The use of soft attention allows the model to generalize to conditions and tasks unseen in the training data. We anticipate that by training this model on a much greater variety of tasks and settings, we will obtain a general system that can turn any demonstrations into robust policies that can accomplish an overwhelming variety of tasks. Videos available at https://bit.ly/nips2017-oneshot .

• The paper introduces a one-shot learning framework that enables robots to acquire new tasks from a single demonstration using RNN and meta-learning techniques.
• It leverages model-agnostic meta-learning (MAML) to quickly adapt learned parameters, effectively handling spatial and temporal variations in demonstrations.
• Empirical results on robotic tasks, such as block stacking and tool usage, demonstrate significant performance improvements over conventional imitation learning methods.

One-Shot Imitation Learning: A Detailed Examination

The paper "One-Shot Imitation Learning," authored by Yan Duan et al., introduces a novel approach in the domain of imitation learning, where a robotic agent can learn complex tasks from a single demonstration. This approach addresses the limitations of traditional imitation learning, which typically requires large datasets of demonstrations to generalize effectively.

Key Contributions

The authors present a framework that enables an agent to perform one-shot learning by utilizing a combination of model architecture and training strategy. The architecture leverages a recurrent neural network (RNN) to encode the demonstrated trajectory and utilizes this encoding to condition the agent's policy during deployment. This approach ensures that the agent retains the ability to adapt its behavior based on varied demonstrations, encapsulating both spatial and temporal variations inherent in the task.

Central to their methodology is the use of a meta-learning technique, which equips the agent with the capability to rapidly assimilate new tasks. The meta-learning approach, particularly model-agnostic meta-learning (MAML), is employed to train the agent's parameters such that they are easily adaptable with minimal updates from the task-specific demonstration.

Numerical Results and Benchmarks

The authors validate their approach through extensive empirical evaluations on a simulated robotic platform. The tasks involve manipulation scenarios such as block stacking and tool usage, which require high precision and adaptability. The results highlight that the proposed one-shot imitation learning framework outperforms baseline methods, achieving success rates significantly higher across various tasks with varying complexity and environmental conditions.

Furthermore, the experiments illustrate the potential of the framework in handling demonstrations with different characteristics, such as changes in perspective and varying speeds of demonstration. This versatility marks a significant improvement over traditional imitation learning techniques, which often struggle with such variations.

Implications and Future Directions

From a theoretical standpoint, the introduction of one-shot learning in robotic imitation learning signifies progress towards more human-like learning capabilities in artificial intelligence systems. The proposed framework illustrates the potential for developing agents that can rapidly adapt to new tasks with minimal prior examples, a necessary feature for real-world applications where obtaining large datasets is impractical.

The practical implications involve the deployment of robots and autonomous systems in environments where interactions are inherently dynamic and unpredictable. For instance, in domestic or industrial settings, the need for robots to quickly learn and adapt to new tasks with singular guidance could improve efficiency and reduce operational overhead.

Looking ahead, future research may explore the integration of this framework with other learning paradigms, such as reinforcement learning, to enhance the adaptability and robustness of agents further. Additionally, extending this approach to multi-agent systems could foster collaborative task execution, pushing the boundaries of what is achievable in robotic learning and autonomy.

In summary, "One-Shot Imitation Learning" provides a substantial step towards improving the adaptability and efficiency of imitation learning systems, presenting both a robust framework and compelling empirical evidence of its efficacy. The paper lays the groundwork for future explorations into efficient, scalable learning mechanisms in robotics and beyond.