Learning to Jump from Pixels
- URL: http://arxiv.org/abs/2110.15344v1
- Date: Thu, 28 Oct 2021 17:53:06 GMT
- Title: Learning to Jump from Pixels
- Authors: Gabriel B. Margolis, Tao Chen, Kartik Paigwar, Xiang Fu, Donghyun Kim,
Sangbae Kim, Pulkit Agrawal
- Abstract summary: We present Depth-based Impulse Control (DIC), a method for synthesizing highly agile visually-guided behaviors.
DIC affords the flexibility of model-free learning but regularizes behavior through explicit model-based optimization of ground reaction forces.
We evaluate the proposed method both in simulation and in the real world.
- Score: 23.17535989519855
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Today's robotic quadruped systems can robustly walk over a diverse range of
rough but continuous terrains, where the terrain elevation varies gradually.
Locomotion on discontinuous terrains, such as those with gaps or obstacles,
presents a complementary set of challenges. In discontinuous settings, it
becomes necessary to plan ahead using visual inputs and to execute agile
behaviors beyond robust walking, such as jumps. Such dynamic motion results in
significant motion of onboard sensors, which introduces a new set of challenges
for real-time visual processing. The requirement for agility and terrain
awareness in this setting reinforces the need for robust control. We present
Depth-based Impulse Control (DIC), a method for synthesizing highly agile
visually-guided locomotion behaviors. DIC affords the flexibility of model-free
learning but regularizes behavior through explicit model-based optimization of
ground reaction forces. We evaluate the proposed method both in simulation and
in the real world.
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