On the Effectiveness of Retrieval, Alignment, and Replay in Manipulation (2312.12345v1)

Abstract: Imitation learning with visual observations is notoriously inefficient when addressed with end-to-end behavioural cloning methods. In this paper, we explore an alternative paradigm which decomposes reasoning into three phases. First, a retrieval phase, which informs the robot what it can do with an object. Second, an alignment phase, which informs the robot where to interact with the object. And third, a replay phase, which informs the robot how to interact with the object. Through a series of real-world experiments on everyday tasks, such as grasping, pouring, and inserting objects, we show that this decomposition brings unprecedented learning efficiency, and effective inter- and intra-class generalisation. Videos are available at https://www.robot-learning.uk/retrieval-alignment-replay.

• The paper demonstrates that decomposing robotic manipulation into retrieval, alignment, and replay stages allows effective learning from a single demonstration per object.
• The framework employs a goal-conditioned policy during the alignment phase to accurately position the robot’s end-effector for improved task execution.
• Experiments on everyday tasks like grasping and pouring confirm the method’s robust generalization without reliance on extensive datasets or external models.

Introduction to the Framework

Imitation learning with visual guidance has traditionally been challenged by its inefficiency, especially when implementing end-to-end behavioral cloning methods. In the reviewed work, an alternative framework is put forth which enhances learning efficiency and facilitates generalization to novel objects and tasks.

Key Principles of the Framework

The proposed framework decomposes the reasoning process into three distinct stages: retrieval, alignment, and replay. This structure means the robot determines what can be done with an object, where interactions should be initiated, and consequently, how to interact. The retrieval process involves identifying the most visually similar training object from a memory buffer and using it to inform subsequent actions. The alignment phase employs a goal-conditioned policy to align the robot's end-effector to the target object. Finally, the replay phase involves performing the learned interactions from stored trajectories.

Advantages of the Approach

The novel aspects of this framework show significant advantages over traditional methods. A set of experiments with everyday tasks, such as grasping and pouring, demonstrates that this decomposition and retrieval strategy outperforms other methods that lack this structure. Particularly telling is the ability of the robot to learn from a single demonstration per object, as opposed to the extensive training data required by other models. This suggests a substantial improvement in learning efficiency. Furthermore, this efficiency translates into effective performance with novel objects, showcasing the framework's robust generalization capabilities. The key distinction of this approach is its absence of reliance on extensive datasets, external cameras, or object models, making the system versatile and more easily deployable in day-to-day environments.

Implications and Future Work

The success of this framework points to the considerable potential for robotic systems that can adapt and learn efficiently from minimal demonstrations. By focusing on retrieval, alignment, and replay, robots could be programmed to conduct a variety of tasks with a higher degree of autonomy and less reliance on human intervention or meticulous programming. While this methodology has shown impressive initial results, it also opens up avenues for research into multi-object interaction, task differentiation, and dealing with real-time feedback tasks that require continual adjustments. Moving forward, this framework could thus usher in a new wave of intelligent, adaptive robots suitable for a myriad of applications.