630 research outputs found
Skeleton2Humanoid: Animating Simulated Characters for Physically-plausible Motion In-betweening
Human motion synthesis is a long-standing problem with various applications
in digital twins and the Metaverse. However, modern deep learning based motion
synthesis approaches barely consider the physical plausibility of synthesized
motions and consequently they usually produce unrealistic human motions. In
order to solve this problem, we propose a system ``Skeleton2Humanoid'' which
performs physics-oriented motion correction at test time by regularizing
synthesized skeleton motions in a physics simulator. Concretely, our system
consists of three sequential stages: (I) test time motion synthesis network
adaptation, (II) skeleton to humanoid matching and (III) motion imitation based
on reinforcement learning (RL). Stage I introduces a test time adaptation
strategy, which improves the physical plausibility of synthesized human
skeleton motions by optimizing skeleton joint locations. Stage II performs an
analytical inverse kinematics strategy, which converts the optimized human
skeleton motions to humanoid robot motions in a physics simulator, then the
converted humanoid robot motions can be served as reference motions for the RL
policy to imitate. Stage III introduces a curriculum residual force control
policy, which drives the humanoid robot to mimic complex converted reference
motions in accordance with the physical law. We verify our system on a typical
human motion synthesis task, motion-in-betweening. Experiments on the
challenging LaFAN1 dataset show our system can outperform prior methods
significantly in terms of both physical plausibility and accuracy. Code will be
released for research purposes at:
https://github.com/michaelliyunhao/Skeleton2HumanoidComment: Accepted by ACMMM202
Imitating human motion using humanoid upper body models
Includes abstract.Includes bibliographical references.This thesis investigates human motion imitation of five different humanoid upper bodies (comprised of the torso and upper limbs) using human dance motion as a case study. The humanoid models are based on five existing humanoids, namely, ARMAR, HRP-2, SURALP, WABIAN-2, and WE-4RII. These humanoids are chosen for their different structures and range of joint motion
Modeling and Design Analysis of Facial Expressions of Humanoid Social Robots Using Deep Learning Techniques
abstract: A lot of research can be seen in the field of social robotics that majorly concentrate on various aspects of social robots including design of mechanical parts and their move- ment, cognitive speech and face recognition capabilities. Several robots have been developed with the intention of being social, like humans, without much emphasis on how human-like they actually look, in terms of expressions and behavior. Fur- thermore, a substantial disparity can be seen in the success of results of any research involving ”humanizing” the robots’ behavior, or making it behave more human-like as opposed to research into biped movement, movement of individual body parts like arms, fingers, eyeballs, or human-like appearance itself. The research in this paper in- volves understanding why the research on facial expressions of social humanoid robots fails where it is not accepted completely in the current society owing to the uncanny valley theory. This paper identifies the problem with the current facial expression research as information retrieval problem. This paper identifies the current research method in the design of facial expressions of social robots, followed by using deep learning as similarity evaluation technique to measure the humanness of the facial ex- pressions developed from the current technique and further suggests a novel solution to the facial expression design of humanoids using deep learning.Dissertation/ThesisMasters Thesis Computer Science 201
Time-Contrastive Networks: Self-Supervised Learning from Video
We propose a self-supervised approach for learning representations and
robotic behaviors entirely from unlabeled videos recorded from multiple
viewpoints, and study how this representation can be used in two robotic
imitation settings: imitating object interactions from videos of humans, and
imitating human poses. Imitation of human behavior requires a
viewpoint-invariant representation that captures the relationships between
end-effectors (hands or robot grippers) and the environment, object attributes,
and body pose. We train our representations using a metric learning loss, where
multiple simultaneous viewpoints of the same observation are attracted in the
embedding space, while being repelled from temporal neighbors which are often
visually similar but functionally different. In other words, the model
simultaneously learns to recognize what is common between different-looking
images, and what is different between similar-looking images. This signal
causes our model to discover attributes that do not change across viewpoint,
but do change across time, while ignoring nuisance variables such as
occlusions, motion blur, lighting and background. We demonstrate that this
representation can be used by a robot to directly mimic human poses without an
explicit correspondence, and that it can be used as a reward function within a
reinforcement learning algorithm. While representations are learned from an
unlabeled collection of task-related videos, robot behaviors such as pouring
are learned by watching a single 3rd-person demonstration by a human. Reward
functions obtained by following the human demonstrations under the learned
representation enable efficient reinforcement learning that is practical for
real-world robotic systems. Video results, open-source code and dataset are
available at https://sermanet.github.io/imitat
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