Helpful DoggyBot: Open-World Object Fetching using Legged Robots and
  Vision-Language Models
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/
66fcb48725ceeb69c30537ea
Qi Wu
claimed_verified
2024-10-16T09:44:34.199Z
670f6cbda4e6ac0ef673530a
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/mtrGsHBXXC1hS0BmWDGio.png
Qi Wu
user
wooqi
66fcb48725ceeb69c30537eb
Zipeng Fu
66fcb48725ceeb69c30537ec
Xuxin Cheng
66fcb48725ceeb69c30537ed
Xiaolong Wang
66fcb48725ceeb69c30537ee
Chelsea Finn
admin_assigned
2024-10-02T09:04:20.818Z
64ac22a9193f0a807deb673d
/avatars/fcac4912678ad3cb6e817d40bdee9aea.svg
Chelsea Finn
user
cbfinn
Helpful DoggyBot: Open-World Object Fetching using Legged Robots and
  Vision-Language Models
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/
A quadruped mobile manipulation system uses a gripper, simulation-trained low-level controller, and vision-language models for object manipulation and command understanding in indoor environments.
front-mounted gripper low-level controller egocentric depth pre-trained vision-language models third-person fisheye camera egocentric RGB camera semantic understanding zero-shot generalization