End-to-End Affordance Learning for Robotic Manipulation
- URL: http://arxiv.org/abs/2209.12941v1
- Date: Mon, 26 Sep 2022 18:24:28 GMT
- Title: End-to-End Affordance Learning for Robotic Manipulation
- Authors: Yiran Geng, Boshi An, Haoran Geng, Yuanpei Chen, Yaodong Yang, Hao
Dong
- Abstract summary: Learning to manipulate 3D objects in an interactive environment has been a challenging problem in Reinforcement Learning.
Visual affordance has shown great prospects in providing object-centric information priors with effective actionable semantics.
In this study, we take advantage of visual affordance by using the contact information generated during the RL training process to predict contact maps of interest.
- Score: 4.405918052597016
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Learning to manipulate 3D objects in an interactive environment has been a
challenging problem in Reinforcement Learning (RL). In particular, it is hard
to train a policy that can generalize over objects with different semantic
categories, diverse shape geometry and versatile functionality. Recently, the
technique of visual affordance has shown great prospects in providing
object-centric information priors with effective actionable semantics. As such,
an effective policy can be trained to open a door by knowing how to exert force
on the handle. However, to learn the affordance, it often requires
human-defined action primitives, which limits the range of applicable tasks. In
this study, we take advantage of visual affordance by using the contact
information generated during the RL training process to predict contact maps of
interest. Such contact prediction process then leads to an end-to-end
affordance learning framework that can generalize over different types of
manipulation tasks. Surprisingly, the effectiveness of such framework holds
even under the multi-stage and the multi-agent scenarios. We tested our method
on eight types of manipulation tasks. Results showed that our methods
outperform baseline algorithms, including visual-based affordance methods and
RL methods, by a large margin on the success rate. The demonstration can be
found at https://sites.google.com/view/rlafford/.
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