6 research outputs found
Functionality-Driven Musculature Retargeting
We present a novel retargeting algorithm that transfers the musculature of a
reference anatomical model to new bodies with different sizes, body
proportions, muscle capability, and joint range of motion while preserving the
functionality of the original musculature as closely as possible. The geometric
configuration and physiological parameters of musculotendon units are estimated
and optimized to adapt to new bodies. The range of motion around joints is
estimated from a motion capture dataset and edited further for individual
models. The retargeted model is simulation-ready, so we can physically simulate
muscle-actuated motor skills with the model. Our system is capable of
generating a wide variety of anatomical bodies that can be simulated to walk,
run, jump and dance while maintaining balance under gravity. We will also
demonstrate the construction of individualized musculoskeletal models from
bi-planar X-ray images and medical examinations.Comment: 15 pages, 20 figure
Learning Expressive Quadrupedal Locomotion Guided by Kinematic Trajectory Generator
Biological quadrupedal systems exhibit a wider range of locomotion skills. In Robotics, quadrupedal systems only exhibit a limited range of locomotion skills. They can be very robust for a single locomotion task, and state-of-the-art algorithms have been designed for walking gaits or use individual policies trained for a single skill. This thesis aimed to study the design of an expressive locomotion controller (different locomotion skills in one policy) for a quadrupedal robot. Different approaches based on Deep Reinforcement Learning have been studied for their recent successes in Robotics and Computer animation. A reference-free and a reference-based approach using solely reward shaping, i.e. specification of the motion through the reward, have been implemented. They produced walking gaits in simulation. Yet, the motions produced by the reference-based approach had limited footstep height and balance issues. The reference-free approach had higher footsteps and fewer base oscillations. Yet, both approaches are hard to adapt when it comes to expressiveness since the motion specification is solely done through reward shaping, which is not intuitive. Finally, inspired by works in computer animation and robotics, an approach based on motion clips for motion specification and general motion tracking has been implemented and produced more natural motions in simulation, i.e. higher footsteps, bigger strides, more base stability hard to generate using reward shaping.M.S
Learning Whole-body Motor Skills for Humanoids
This paper presents a hierarchical framework for Deep Reinforcement Learning
that acquires motor skills for a variety of push recovery and balancing
behaviors, i.e., ankle, hip, foot tilting, and stepping strategies. The policy
is trained in a physics simulator with realistic setting of robot model and
low-level impedance control that are easy to transfer the learned skills to
real robots. The advantage over traditional methods is the integration of
high-level planner and feedback control all in one single coherent policy
network, which is generic for learning versatile balancing and recovery motions
against unknown perturbations at arbitrary locations (e.g., legs, torso).
Furthermore, the proposed framework allows the policy to be learned quickly by
many state-of-the-art learning algorithms. By comparing our learned results to
studies of preprogrammed, special-purpose controllers in the literature,
self-learned skills are comparable in terms of disturbance rejection but with
additional advantages of producing a wide range of adaptive, versatile and
robust behaviors.Comment: 2018 IEEE-RAS 18th International Conference on Humanoid Robots
(Humanoids