Helpful DoggyBot: Open-World Object Fetching using Legged Robots and
  Vision-Language Models
Authors: name: Qi Wu, name: Zipeng Fu, name: Xuxin Cheng, name: Xiaolong Wang, name: Chelsea Finn

Learning-based methods have achieved strong performance for quadrupedal
locomotion. However, several challenges prevent quadrupeds from learning
helpful indoor skills that require interaction with environments and humans:
lack of end-effectors for manipulation, limited semantic understanding using
only simulation data, and low traversability and reachability in indoor
environments. We present a system for quadrupedal mobile manipulation in indoor
environments. It uses a front-mounted gripper for object manipulation, a
low-level controller trained in simulation using egocentric depth for agile
skills like climbing and whole-body tilting, and pre-trained vision-language
models (VLMs) with a third-person fisheye and an egocentric RGB camera for
semantic understanding and command generation. We evaluate our system in two
unseen environments without any real-world data collection or training. Our
system can zero-shot generalize to these environments and complete tasks, like
following user's commands to fetch a randomly placed stuff toy after climbing
over a queen-sized bed, with a 60% success rate. Project website:
https://helpful-doggybot.github.io/