Measuring and Improving Constitutional Adherence
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Credibility Rating
Good quality. Reputable source with community review or editorial standards, but less rigorous than peer-reviewed venues.
Rating inherited from publication venue: arXiv
This robotics paper is not directly related to AI safety or alignment; the URL and title metadata appear mismatched with the actual content, which focuses on robot learning efficiency rather than constitutional AI adherence.
Paper Details
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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.
Summary
This paper proposes a three-phase decomposition framework for robotic manipulation imitation learning, separating reasoning into retrieval (what to do), alignment (where to interact), and replay (how to interact). Tested on real-world tasks like grasping and pouring, the approach achieves superior learning efficiency and generalization to novel objects compared to end-to-end behavioral cloning.
Key Points
- •Decomposes imitation learning into three specialist phases: retrieval, alignment, and replay, rather than monolithic end-to-end control.
- •Retrieval identifies the most visually similar training object; alignment positions the end-effector; replay executes learned demonstration velocities.
- •Enables one-shot generalization to novel objects and novel object classes without requiring large numbers of human demonstrations.
- •Validated through real-world robotic experiments on everyday manipulation tasks including grasping, pouring, and inserting.
- •Addresses the notorious inefficiency of behavioral cloning from visual observations by modularizing the reasoning process.
Cited by 1 page
| Page | Type | Quality |
|---|---|---|
| Constitutional AI | Approach | 70.0 |
Cached Content Preview
# On the Effectiveness of Retrieval, Alignment, and Replay in Manipulation
Norman Di Palo1 and Edward Johns1Manuscript received: August 21, 2023; Revised November 9, 2023; Accepted December 8, 2023 .This paper was recommended for publication by Editor Aleksandra Faust upon evaluation of the Associate Editor and Reviewers’ comments.
This work was supported by the Royal Academy of Engineering under the Research Fellowship Scheme.1Norman Di Palo and Edward Johns are with the Robot Learning Lab at Imperial College London n.di-palo20@imperial.ac.ukDigital Object Identifier (DOI): see top of this page.
###### 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](https://www.robot-learning.uk/retrieval-alignment-replay "").
###### Index Terms:
Deep Learning in Grasping and Manipulation, Imitation Learning, Learning from Demonstration
Figure 1: An overview of the framework we study, showing the retrieval, alignment, and replay phases. Together, these enable one-shot imitation learning without prior object knowledge, as well as generalisation to novel objects and novel classes.
## I Introduction
In this paper, we study the problem of teaching a robot how to interact with a set of training objects, and then generalising these learned behaviours to novel objects and novel classes of objects. Today’s dominant paradigm in recent literature is to address this with end-to-end behavioural cloning \[ [4](https://ar5iv.labs.arxiv.org/html/2312.12345#bib.bib4 "")\]. However, to generalise everyday manipulation skills to many different objects, such techniques require a very large number of human demonstrations, which is slow and expensive.
But as an alternative to monolithic, end-to-end control, we can decompose reasoning into three distinct, specialist modes of reasoning. Firstly, what can a robot do with an object? Secondly, where should a robot interact with an object? And thirdly, how should a robot interact with an object? Our hypothesis is that this decomposition might be more optimal than expecting a single control policy to be able to reason simultaneously about all three modes.
To achieve these three modes o
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