Imitation from Observation: Learning to Imitate Behaviors from Raw Video via Context Translation (1707.03374v2)

Abstract: Imitation learning is an effective approach for autonomous systems to acquire control policies when an explicit reward function is unavailable, using supervision provided as demonstrations from an expert, typically a human operator. However, standard imitation learning methods assume that the agent receives examples of observation-action tuples that could be provided, for instance, to a supervised learning algorithm. This stands in contrast to how humans and animals imitate: we observe another person performing some behavior and then figure out which actions will realize that behavior, compensating for changes in viewpoint, surroundings, object positions and types, and other factors. We term this kind of imitation learning "imitation-from-observation," and propose an imitation learning method based on video prediction with context translation and deep reinforcement learning. This lifts the assumption in imitation learning that the demonstration should consist of observations in the same environment configuration, and enables a variety of interesting applications, including learning robotic skills that involve tool use simply by observing videos of human tool use. Our experimental results show the effectiveness of our approach in learning a wide range of real-world robotic tasks modeled after common household chores from videos of a human demonstrator, including sweeping, ladling almonds, pushing objects as well as a number of tasks in simulation.

Insights into Imitation Learning and Network Architecture

The paper presents a detailed investigation into network architectures and strategies for imitation learning, particularly focusing on translating learned behaviors from simulation environments to real-world applications. The paper utilizes a modular approach involving encoders, a translation module, and decoders to facilitate the imitation of complex tasks such as reaching, pushing, sweeping, and striking.

Network Architecture

The network architecture described includes two primary encoders, $\text{Enc}_1$ and $\text{Enc}_2$, designed for extracting features from input data through a series of stride-2 convolutions with a $5\times5$ kernel. These layers have progressively increasing filter sizes of 64, 128, 256, and 512, followed by fully connected layers of size 1024. LeakyReLU activations with a leak of 0.2 enhance the non-linearity of the models. The translation module uses a concatenated input $z_1, z_2$, and it similarly outputs through a hidden layer of size 1024. For decoding the encoded representations, a series of fractionally-strided convolutions is applied, showcasing a reduction in filter sizes as it progresses to the output layer. Notably, skip connections from the context encoder to the decoder improve information flow and model performance.

When addressing real-world images, the architecture undergoes modifications, reducing the complexity to feature layers of size 100 and employing strides of 1 and 2 in the encoder's convolutional layers. Moreover, dropout is applied during training to enhance generalization capabilities, with weights shared between the two encoders to encourage coherent feature learning.

Training Regimen and Evaluation

The training setup employs the ADAM optimizer with a learning rate of $10^{-4}$ across a diverse dataset comprising thousands of videos for various tasks both in simulated and real environments. This robust dataset underpins the evaluation of the network's ability to generalize learned policies across different contexts.

An ablation paper outlined in the paper rigorously tests various components of the translation and reward functions to determine their impact on imitation performance. The paper methodically removes elements like translation cost $\mathcal{L}_{\text{trans}}$, model losses $\mathcal{L}_{\text{rec}}$, and $\mathcal{L}_{\text{align}}$, revealing substantial performance degradation across tasks when these components are omitted. This result underscores the essentiality of each component in maintaining the fidelity of learned behaviors.

Implications and Future Directions

The research contributes valuable insights into the design of neural network architectures for imitation learning, emphasizing the necessity of diversified loss functions and reward components. By demonstrating the effectiveness of their approach in both simulated and real-world tasks, the paper sets a foundation for further exploration into more intricate and detailed imitation learning scenarios.

Future developments could extend to refining the architectures for more efficient real-world adaptability or exploring the impact of alternative optimization strategies. Additionally, integrating unsupervised or self-supervised learning methods may enhance the network's ability to abstract and transfer knowledge across disparate domains.

Overall, this paper informs on the structural and functional considerations critical for effective imitation learning, and it opens avenues for advancing the translation of learned policies into practical, real-world applications.