SAGCI-System: Towards Sample-Efficient, Generalizable, Compositional,
and Incremental Robot Learning
- URL: http://arxiv.org/abs/2111.14693v2
- Date: Tue, 30 Nov 2021 04:27:23 GMT
- Title: SAGCI-System: Towards Sample-Efficient, Generalizable, Compositional,
and Incremental Robot Learning
- Authors: Jun Lv, Qiaojun Yu, Lin Shao, Wenhai Liu, Wenqiang Xu, Cewu Lu
- Abstract summary: We introduce a systematic learning framework called SAGCI-system towards achieving the above four requirements.
Our system first takes the raw point clouds gathered by the camera mounted on the robot's wrist as the inputs and produces initial modeling of the surrounding environment represented as a URDF.
The robot then utilizes the interactive perception to interact with the environments to online verify and modify the URDF.
- Score: 41.19148076789516
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Building general-purpose robots to perform an enormous amount of tasks in a
large variety of environments at the human level is notoriously complicated. It
requires the robot learning to be sample-efficient, generalizable,
compositional, and incremental. In this work, we introduce a systematic
learning framework called SAGCI-system towards achieving these above four
requirements. Our system first takes the raw point clouds gathered by the
camera mounted on the robot's wrist as the inputs and produces initial modeling
of the surrounding environment represented as a URDF. Our system adopts a
learning-augmented differentiable simulation that loads the URDF. The robot
then utilizes the interactive perception to interact with the environments to
online verify and modify the URDF. Leveraging the simulation, we propose a new
model-based RL algorithm combining object-centric and robot-centric approaches
to efficiently produce policies to accomplish manipulation tasks. We apply our
system to perform articulated object manipulation, both in the simulation and
the real world. Extensive experiments demonstrate the effectiveness of our
proposed learning framework. Supplemental materials and videos are available on
https://sites.google.com/view/egci.
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