Language Conditioned Imitation Learning over Unstructured Data (2005.07648v2)

Abstract: Natural language is perhaps the most flexible and intuitive way for humans to communicate tasks to a robot. Prior work in imitation learning typically requires each task be specified with a task id or goal image -- something that is often impractical in open-world environments. On the other hand, previous approaches in instruction following allow agent behavior to be guided by language, but typically assume structure in the observations, actuators, or language that limit their applicability to complex settings like robotics. In this work, we present a method for incorporating free-form natural language conditioning into imitation learning. Our approach learns perception from pixels, natural language understanding, and multitask continuous control end-to-end as a single neural network. Unlike prior work in imitation learning, our method is able to incorporate unlabeled and unstructured demonstration data (i.e. no task or language labels). We show this dramatically improves language conditioned performance, while reducing the cost of language annotation to less than 1% of total data. At test time, a single language conditioned visuomotor policy trained with our method can perform a wide variety of robotic manipulation skills in a 3D environment, specified only with natural language descriptions of each task (e.g. "open the drawer...now pick up the block...now press the green button..."). To scale up the number of instructions an agent can follow, we propose combining text conditioned policies with large pretrained neural LLMs. We find this allows a policy to be robust to many out-of-distribution synonym instructions, without requiring new demonstrations. See videos of a human typing live text commands to our agent at language-play.github.io

Overview of "Template Paper for the Robotics: Science and Systems Conference"

The manuscript titled "Template Paper for the Robotics: Science and Systems Conference" serves as a fundamental guide primarily intended to assist authors in preparing their submissions using the \LaTeX\ document preparation system, specifically, leveraging the IEEEtran.cls version 1.7a and later. This paper ostensibly functions as a procedural framework rather than presenting novel empirical findings or theoretical advancements in robotics. It caters to authors intending to submit to the Robotics: Science and Systems conference by elucidating the standardized format and crucial components of assembling a paper in compliance with specified guidelines.

Structural Elements and Formatting

The paper sections are crafted to facilitate users who are integrating the IEEEtran document class into their writing workflow. This includes introductory information, sectioning templates, and instructions for utilizing specific bibliographic and referencing styles, notably the natbib.sty with the plainnat.bst style. This guideline is crucial for maintaining consistency in citation style, enhancing readability, and fostering professional presentation across conference submissions.

The section on RSS citations underscores the utility of \verb!\citet! commands, streamlining the citation process by offering an elegant and reader-friendly solution. This is an essential semantic element when framing scholarly work, ensuring citations are embedded seamlessly within the narrative text.

Enhancements through Hyperlinks

A noteworthy integration in this template is the encouragement to employ hyperlinks within references, leveraging PDF viewers' capabilities to enhance accessibility. Authors are advised to link references directly to online sources, ideally archival or publisher sites, thus facilitating immediate access to primary sources. While technically straightforward, this feature significantly augments the reader's ability to engage with cited literature directly and efficiently, proving vital in a landscape where accessibility of digital content is paramount.

Implications and Future Directions

While the paper does not directly contribute to empirical insights or propose theoretical models, its implications lie in the field of scholarly communication within the robotics field. By delineating a coherent framework for document preparation, this template indirectly supports the dissemination of research outputs, enhancing the clarity and presentation quality of submissions to the Robotics: Science and Systems community.

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In conclusion, while this template paper operates primarily within an administrative scope, its significance in fostering consistency, professionalism, and enhanced communication in robotics research papers is non-trivial. These structural guides form the backbone of effective scholarly dissemination, thereby underpinning the broader impact of research contributions in the field.