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Demonstrations and natural language instructions are two common ways to specify and teach robots novel tasks. However, for many complex tasks, a demonstration or language instruction alone contains ambiguities, preventing tasks from being specified clearly. In such cases, a combination of both a demonstration and an instruction more concisely and effectively conveys the task to the robot than either modality alone. To instantiate this problem setting, we train a single multi-task policy on a few hundred challenging robotic pick-and-place tasks and propose DeL-TaCo (Joint Demo-Language Task Conditioning), a method for conditioning a robotic policy on task embeddings comprised of two components: a visual demonstration and a language instruction. By allowing these two modalities to mutually disambiguate and clarify each other during novel task specification, DeL-TaCo (1) substantially decreases the teacher effort needed to specify a new task and (2) achieves better generalization performance on novel objects and instructions over previous task-conditioning methods. To our knowledge, this is the first work to show that simultaneously conditioning a multi-task robotic manipulation policy on both demonstration and language embeddings improves sample efficiency and generalization over conditioning on either modality alone. See additional materials at https://sites.google.com/view/del-taco-learning
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Geometric Analysis and Metric Learning of Instruction Embeddings
Embeddings for instructions have been shown to be essential for software reverse engineering and automated program analysis. However, due to the complexity of dependencies and inherent variability of instructions, instruction embeddings using models that are successful for natural language processing may not be effective. In this paper, we perform geometric analysis of instruction embeddings at the token level and instruction family level, showing much greater variability and leading to degraded performance on intrinsic analyses. Then we propose to use metric learning to improve the relationships among instructions using triplet loss. Our results on a large dataset of instruction groups shows significant improvements. We also provide a theoretical analysis of the instruction embeddings by looking at the BERT components and characteristics of inner-product matrices for attention in the transformer blocks. The code will be available publicly after the paper is accepted for publication.
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- PAR ID:
- 10376712
- Date Published:
- Journal Name:
- International Joint Conference on Neural Networks
- Page Range / eLocation ID:
- 1 to 8
- 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
- Conference Paper:
- https://doi.org/10.1109/IJCNN55064.2022.9892426
