From explanation to synthesis: Compositional program induction for learning from demonstration
Hybrid systems are a compact and natural mechanism with which to address problems in robotics. This work introduces an approach to learning hybrid systems from demonstrations, with an emphasis on extracting models that are explicitly verifiable and easily interpreted by robot operators. We fit a sequence of controllers using sequential importance sampling under a generative switching proportional controller task model. Here, we parameterise controllers using a proportional gain and a visually verifiable joint angle goal. Inference under this model is challenging, but we address this by introducing an attribution prior extracted from a neural end-to-end visuomotor control model. Given the sequence of controllers comprising a task, we simplify the trace using grammar parsing strategies, taking advantage of the sequence compositionality, before grounding the controllers by training perception networks to predict goals given images. Using this approach, we are successfully able to induce a program for a visuomotor reaching task involving loops and conditionals from a single demonstration and a neural end-to-end model. In addition, we are able to discover the program used for a tower building task. We argue that computer program-like control systems are more interpretable than alternative end-to-end learning approaches, and that hybrid systems inherently allow for better generalisation across task configurations.
Code (0)
등록된 구현이 없습니다.
Tasks
Program inductionSimilar Papers 제목 키워드 기반
Neural Task Programming: Learning to Generalize Across Hierarchical Tasks
In this work, we propose a novel robot learning framework called Neural Task Programming (NTP), which bridges the idea of few-shot learning from demonstration and neural program induction. NTP takes as input a task speci…
Few-Shot LearningProgram inductionRobot ManipulationSemi-supervised Learning From Demonstration Through Program Synthesis: An Inspection Robot Case Study
Semi-supervised learning improves the performance of supervised machine learning by leveraging methods from unsupervised learning to extract information not explicitly available in the labels. Through the design of a sys…
ClusteringcounterfactualDecision MakingModel Predictive Control+1CESAR: Automatic Induction of Compositional Instructions for Multi-turn Dialogs
Instruction-based multitasking has played a critical role in the success of large language models (LLMs) in multi-turn dialog applications. While publicly available LLMs have shown promising performance, when exposed to …
Learning Web-based Procedures by Reasoning over Explanations and Demonstrations in Context
We explore learning web-based tasks from a human teacher through natural language explanations and a single demonstration. Our approach investigates a new direction for semantic parsing that models explaining a demonstra…
Language AcquisitionProgram SynthesisSemantic ParsingCompositional diversity in visual concept learning
Humans leverage compositionality to efficiently learn new concepts, understanding how familiar parts can combine together to form novel objects. In contrast, popular computer vision models struggle to make the same types…
DiversityProgram induction