Learning Generalizable Feature Fields for Mobile Manipulation
Authors: name: Ri-Zhao Qiu, name: Yafei Hu, name: Ge Yang, name: Yuchen Song, name: Yang Fu, name: Jianglong Ye, name: Jiteng Mu, name: Ruihan Yang, name: Nikolay Atanasov, name: Sebastian Scherer, name: Xiaolong Wang

An open problem in mobile manipulation is how to represent objects and scenes
in a unified manner, so that robots can use it both for navigating in the
environment and manipulating objects. The latter requires capturing intricate
geometry while understanding fine-grained semantics, whereas the former
involves capturing the complexity inherit to an expansive physical scale. In
this work, we present GeFF (Generalizable Feature Fields), a scene-level
generalizable neural feature field that acts as a unified representation for
both navigation and manipulation that performs in real-time. To do so, we treat
generative novel view synthesis as a pre-training task, and then align the
resulting rich scene priors with natural language via CLIP feature
distillation. We demonstrate the effectiveness of this approach by deploying
GeFF on a quadrupedal robot equipped with a manipulator. We evaluate GeFF's
ability to generalize to open-set objects as well as running time, when
performing open-vocabulary mobile manipulation in dynamic scenes.