Adaptive Tracking of a Single-Rigid-Body Character in Various Environments
DescriptionSince the introduction of DeepMimic [Peng et al. 2018], subsequent research
has focused on expanding the repertoire of simulated motions across various
scenarios. In this study, we propose an alternative approach for this goal,
a deep reinforcement learning method based on the simulation of a single-
rigid-body character. Using the centroidal dynamics model (CDM) to express
the full-body character as a single rigid body (SRB) and training a policy to
track a reference motion, we can obtain a policy that is capable of adapting
to various unobserved environmental changes and controller transitions
without requiring any additional learning. Due to the reduced dimension
of state and action space, the learning process is sample-efficient. The final
full-body motion is kinematically generated in a physically plausible way,
based on the state of the simulated SRB character. The SRB simulation is
formulated as a quadratic programming (QP) problem, and the policy outputs
an action that allows the SRB character to follow the reference motion. We
demonstrate that our policy, efficiently trained within 30 minutes on an
ultraportable laptop, has the ability to cope with environments that have
not been experienced during learning, such as running on uneven terrain
or pushing a box, and transitions between learned policies, without any
additional learning.
has focused on expanding the repertoire of simulated motions across various
scenarios. In this study, we propose an alternative approach for this goal,
a deep reinforcement learning method based on the simulation of a single-
rigid-body character. Using the centroidal dynamics model (CDM) to express
the full-body character as a single rigid body (SRB) and training a policy to
track a reference motion, we can obtain a policy that is capable of adapting
to various unobserved environmental changes and controller transitions
without requiring any additional learning. Due to the reduced dimension
of state and action space, the learning process is sample-efficient. The final
full-body motion is kinematically generated in a physically plausible way,
based on the state of the simulated SRB character. The SRB simulation is
formulated as a quadratic programming (QP) problem, and the policy outputs
an action that allows the SRB character to follow the reference motion. We
demonstrate that our policy, efficiently trained within 30 minutes on an
ultraportable laptop, has the ability to cope with environments that have
not been experienced during learning, such as running on uneven terrain
or pushing a box, and transitions between learned policies, without any
additional learning.
Event Type
Technical Papers
TimeTuesday, 12 December 20239:30am - 12:45pm
LocationDarling Harbour Theatre, Level 2 (Convention Centre)
