One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning (1802.01557v1)

Abstract: Humans and animals are capable of learning a new behavior by observing others perform the skill just once. We consider the problem of allowing a robot to do the same -- learning from a raw video pixels of a human, even when there is substantial domain shift in the perspective, environment, and embodiment between the robot and the observed human. Prior approaches to this problem have hand-specified how human and robot actions correspond and often relied on explicit human pose detection systems. In this work, we present an approach for one-shot learning from a video of a human by using human and robot demonstration data from a variety of previous tasks to build up prior knowledge through meta-learning. Then, combining this prior knowledge and only a single video demonstration from a human, the robot can perform the task that the human demonstrated. We show experiments on both a PR2 arm and a Sawyer arm, demonstrating that after meta-learning, the robot can learn to place, push, and pick-and-place new objects using just one video of a human performing the manipulation.

• The paper introduces a meta-learning approach enabling robots to generalize from a single human demonstration for various manipulation tasks.
• It leverages domain adaptation to overcome differences between human and robot data without relying on explicit pose correspondences.
• The approach achieves robust performance on tasks like placing, pushing, and pick-and-place across PR2 and Sawyer robotic platforms.

Overview of "One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning"

This paper addresses the challenge of one-shot imitation learning in robotics by leveraging domain-adaptive meta-learning (DAML). The authors present a novel approach that enables robots to learn manipulation skills by observing a single video demonstration of a human, even under significant domain shifts. Traditional robotic imitation learning methods frequently demand numerous demonstrations and a complex mapping between human actions and robot actions, often relying on structured input such as explicit pose data. This work seeks to circumvent these requirements by applying meta-learning techniques to facilitate domain adaptation from human videos to robot actions.

Key Contributions

1. Meta-Learning for Imitation: The paper introduces a meta-learning framework that allows a robot to build a prior over a range of tasks using a combination of human and robot demonstration data. This meta-learning framework forms the basis for the robot's ability to generalize from a single human demonstration to new tasks.
2. Domain Adaptation: The authors propose addressing the domain shift problem—differences in perception and embodiment between human demonstrators and robots—using a data-driven approach. This methodology does not rely on manually specified correspondences, instead learning the task structure from a multitude of demonstrations across different domains.
3. Learned Temporal Adaptation Objective: A critical innovation in this work is the introduction of a temporal adaptation objective within the meta-learning framework. This learned objective uses temporal convolutions to capture information across sequences in the demonstration data, facilitating the inference of task-relevant information from video inputs without direct action labels.

Experimental Results

The paper reports robust results demonstrating the efficacy of their approach on two robotic platforms: the PR2 and the Sawyer arms. The authors conduct experiments on tasks such as placing, pushing, and pick-and-place, using novel objects not seen during training. The robots, after undergoing meta-training, effectively execute tasks based on one-shot human video demonstrations. The quantitative results indicate a notable advantage of the DAML technique over baseline models, such as contextual policies and domain-adaptive LSTM-based methods.

Implications and Future Directions

The ability to infer robot actions from human demonstrations without the need for action labels or explicit human pose data has significant implications. This work contributes to the field by reducing the dependence on large datasets of robot-centric demonstrations, potentially lowering the cost and complexity of training robotic systems. The approach is particularly promising for tasks involving new objects and environments, suggesting broad applicability across various domains where quick adaptation is critical.

In the future, increasing model capacity or integrating more comprehensive datasets could further extend this technique's capabilities to learn novel motion patterns beyond those observed during meta-training. Additionally, exploring this approach in diverse robotic applications, including service robots and autonomous systems, could highlight the versatility and practical utility of DAML in real-world scenarios.

In conclusion, this paper provides a compelling framework for one-shot robotic imitation learning by synergizing meta-learning and domain adaptation, paving the way for more adaptable and capable robotic systems that learn effectively from human demonstrations.