02/02/2021

Learning to Sit: Synthesizing Human-Chair Interactions via Hierarchical Control

Yu-Wei Chao, Jimei Yang, Weifeng Chen, Jia Deng

Keywords:

Abstract Paper Similar Papers

Abstract: Recent progress on physics-based character animation has shown impressive breakthroughs on human motion synthesis, through imitating motion capture data via deep reinforcement learning. However, results have mostly been demonstrated on imitating a single distinct motion pattern, and do not generalize to interactive tasks that require flexible motion patterns due to varying human-object spatial configurations. To bridge this gap, we focus on one class of interactive tasks---sitting onto a chair. We propose a hierarchical reinforcement learning framework which relies on a collection of subtask controllers trained to imitate simple, reusable mocap motions, and a meta controller trained to execute the subtasks properly to complete the main task. We experimentally demonstrate the strength of our approach over different non-hierarchical and hierarchical baselines. We also show that our approach can be applied to motion prediction given an image input. A supplementary video can be found at https://youtu.be/3CeN0OGz2cA.

The video of this talk cannot be embedded. You can watch it here:
https://slideslive.com/38948694
(Link will open in new window)
 0
 0
 0
 0
  Share    
This is an embedded video. Talk and the respective paper are published at AAAI 2021 virtual conference. If you are one of the authors of the paper and want to manage your upload, see the question "My papertalk has been externally embedded..." in the FAQ section.

Comments

Post Comment
no comments yet
code of conduct: tbd Characters remaining: 140

Similar Papers

 0
 0
 0
 0
 9:42 
 0
 0
 0
 0
 5:01 
 0
 0
 0
 0
 5:07 
 0
 0
 0
 0
 8:29 
 0
 0
 0
 0
 5:03 
 0
 0
 0
 0
 4:59 
 0
 0
 0
 0
 5:26 
 0
 0
 0
 0
 4:39 
 0
 0
 0
 0
 3:13 
 0
 0
 0
 0
 5:06 
 0
 0
 0
 0
 5:05 
 0
 0
 0
 0
 8:13 
 0
 0
 0
 0
 10:03 
 0
 0
 0
 0
 9:50 
 0
 0
 0
 0
 3:22 
 0
 0
 0
 0
 10:03 
 0
 0
 0
 0
 1:01 
 0
 0
 0
 0
 1:00 
 0
 0
 0
 0
 5:01 
 0
 0
 0
 0
 8:39 
 0
 0
 0
 0
 3:16 
 0
 0
 0
 0
 4:57 
 0
 0
 0
 0
 3:20 
 0
 0
 0
 0
 5:16 
 0
 0
 0
 0
 5:01 
 0
 0
 0
 0
 1:01 
 0
 0
 0
 0
 5:01 
 0
 0
 0
 0
 19:55 
 0
 0
 0
 0
 4:59 
 0
 0
 0
 0
 14:42 
 0
 0
 0
 0
 4:56 
 0
 0
 0
 0
 4:58 
 0
 0
 0
 0
 1:01 
 0
 0
 0
 0
 5:03 
 0
 0
 0
 0
 4:57 
 0
 0
 0
 0
 5:47 
 0
 0
 0
 0
 13:41 
 0
 0
 0
 0
 5:13 
 0
 0
 0
 0
 16:08 
 0
 0
 0
 0
 1:03