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Language is compositional; an instruction can ex- press multiple relation constraints to hold among objects in a scene that a robot is tasked to rearrange. Our focus in this work is an instructable scene-rearranging framework that gen- eralizes to longer instructions and to spatial concept compositions never seen at training time. We propose to represent language- instructed spatial concepts with energy functions over relative object arrangements. A language parser maps instructions to corresponding energy functions and an open-vocabulary visual- language model grounds their arguments to relevant objects in the scene. We generate goal scene configurations by gradient descent on the sum of energy functions, one per language predi- cate in the instruction. Local vision-based policies then re-locate objects to the inferred goal locations. We test our model on es- tablished instruction-guided manipulation benchmarks, as well as benchmarks of compositional instructions we introduce. We show our model can execute highly compositional instructions zero-shot in simulation and in the real world. It outperforms language- to-action reactive policies and Large Language Model planners by a large margin, especially for long instructions that involve compositions of multiple spatial concepts. Simulation and real- world robot execution videos, as well as our code and datasets are publicly available on our website: https://ebmplanner.github.io.
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A Modular Vision Language Navigation and Manipulation Framework for Long Horizon Compositional Tasks in Indoor Environment
In this paper we propose a new framework—MoViLan (Modular Vision and Language) for execution of visually grounded natural language instructions for day to day indoor household tasks. While several data-driven, end-to-end learning frameworks have been proposed for targeted navigation tasks based on the vision and language modalities, performance on recent benchmark data sets revealed the gap in developing comprehensive techniques for long horizon, compositional tasks (involving manipulation and navigation) with diverse object categories, realistic instructions and visual scenarios with non reversible state changes. We propose a modular approach to deal with the combined navigation and object interaction problem without the need for strictly aligned vision and language training data (e.g., in the form of expert demonstrated trajectories). Such an approach is a significant departure from the traditional end-to-end techniques in this space and allows for a more tractable training process with separate vision and language data sets. Specifically, we propose a novel geometry-aware mapping technique for cluttered indoor environments, and a language understanding model generalized for household instruction following. We demonstrate a significant increase in success rates for long horizon, compositional tasks over recent works on the recently released benchmark data set -ALFRED.
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- Award ID(s):
- 1932505
- PAR ID:
- 10358025
- Date Published:
- Journal Name:
- Frontiers in Robotics and AI
- Volume:
- 9
- ISSN:
- 2296-9144
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3389/frobt.2022.930486
