News

[09.16.2022] The work has been accepted by NeurIPS 2022 (Datasets and Benchmarks) !

Abstract

Benefiting from language flexibility and compositionality, humans naturally intend to use language to command an embodied agent for complex tasks such as navigation and object manipulation. In this work, we aim to fill the blank of the last mile of embodied agents---object manipulation by following human guidance, e.g., “move the red mug next to the box while keeping it upright.” To this end, we introduce an Automatic Manipulation Solver (AMSolver) simulator and build a Vision-and-Language Manipulation benchmark (VLMbench) based on it, containing various language instructions on categorized robotic manipulation tasks. Specifically, modular rule-based task templates are created to automatically generate robot demonstrations with language instructions, consisting of diverse object shapes and appearances, action types, and motion constraints. We also develop a keypoint-based model 6D-CLIPort to deal with multi-view observations and language input and output a sequence of 6 degrees of freedom (DoF) actions. We hope the new simulator and benchmark will facilitate future research on language-guided robotic manipulation.

Citation

@inproceedings{

zheng2022vlmbench,

title={{VLM}bench: A Compositional Benchmark for Vision-and-Language Manipulation},

author={Kaizhi Zheng and Xiaotong Chen and Odest Jenkins and Xin Eric Wang},

booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},

year={2022},

url={https://openreview.net/forum?id=NAYoSV3tk9}

}