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DTSTAMP:20260114T163644Z
LOCATION:Meeting Room C4.11\, Level 4 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231215T161000
DTEND;TZID=Australia/Melbourne:20231215T162000
UID:siggraphasia_SIGGRAPH Asia 2023_sess139_papers_523@linklings.com
SUMMARY:Adaptive Tracking of a Single-Rigid-Body Character in Various Envi
 ronments
DESCRIPTION:Taesoo Kwon, Taehong Gu, Jaewon Ahn, and Yoonsang Lee (Hanyang
  University)\n\nSince the introduction of DeepMimic [Peng et al. 2018], su
 bsequent research\nhas focused on expanding the repertoire of simulated mo
 tions across various\nscenarios. In this study, we propose an alternative 
 approach for this goal,\na deep reinforcement learning method based on the
  simulation of a single-\nrigid-body character. Using the centroidal dynam
 ics model (CDM) to express\nthe full-body character as a single rigid body
  (SRB) and training a policy to\ntrack a reference motion, we can obtain a
  policy that is capable of adapting\nto various unobserved environmental c
 hanges and controller transitions\nwithout requiring any additional learni
 ng. Due to the reduced dimension\nof state and action space, the learning 
 process is sample-efficient. The final\nfull-body motion is kinematically 
 generated in a physically plausible way,\nbased on the state of the simula
 ted SRB character. The SRB simulation is\nformulated as a quadratic progra
 mming (QP) problem, and the policy outputs\nan action that allows the SRB 
 character to follow the reference motion. We\ndemonstrate that our policy,
  efficiently trained within 30 minutes on an\nultraportable laptop, has th
 e ability to cope with environments that have\nnot been experienced during
  learning, such as running on uneven terrain\nor pushing a box, and transi
 tions between learned policies, without any\nadditional learning.\n\nRegis
 tration Category: Full Access\n\nSession Chair: Yuting Ye (Reality Labs Re
 search, Meta; Meta)\n\n
URL:https://asia.siggraph.org/2023/full-program?id=papers_523&sess=sess139
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