4,097 research outputs found
Tasks prioritization for whole-body realtime imitation of human motion by humanoid robots
International audienceThis paper deals with on-line motion imitation of a human being by a humanoid robot using inverse kinematics (IK). First, the human observed trajectories are scaled in order to match the robot geometric and kinematic description. Second, a task prioritization process is defined using both equality and minimized constraints in the robot IK model, with four tasks: balance management, end-effectors tracking, joint limits avoidance and staying close to the human joint trajectories. The method was validated using the humanoid robot NAO
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
Human Motion Transfer on Humanoid Robot
The aim of this thesis is to transfer human motion to a humanoid robot online. In the first part of this work, the human motion recorded by a motion capture system is analyzed to extract salient features that are to be transferred on the humanoid robot. We introduce the humanoid normalized model as the set of motion properties. In the second part of this work, the robot motion that includes the human motion features is computed using the inverse kinematics with priority. In order to transfer the motion properties a stack of tasks is predefined. Each motion property in the humanoid normalized model corresponds to one target in the stack of tasks. We propose a framework to transfer human motion online as close as possible to a human motion performance for the upper body. Finally, we study the problem of transfering feet motion. In this study, the motion of feet is analyzed to extract the Euclidean trajectories adapted to the robot. Moreover, the trajectory of the center of mass which ensures that the robot does not fall is calculated from the feet positions and the inverse pendulum model of the robot. Using this result, it is possible to achieve complete imitation of upper body movements and including feet motio
Motion Imitation Based on Sparsely Sampled Correspondence
Existing techniques for motion imitation often suffer a certain level of
latency due to their computational overhead or a large set of correspondence
samples to search. To achieve real-time imitation with small latency, we
present a framework in this paper to reconstruct motion on humanoids based on
sparsely sampled correspondence. The imitation problem is formulated as finding
the projection of a point from the configuration space of a human's poses into
the configuration space of a humanoid. An optimal projection is defined as the
one that minimizes a back-projected deviation among a group of candidates,
which can be determined in a very efficient way. Benefited from this
formulation, effective projections can be obtained by using sparse
correspondence. Methods for generating these sparse correspondence samples have
also been introduced. Our method is evaluated by applying the human's motion
captured by a RGB-D sensor to a humanoid in real-time. Continuous motion can be
realized and used in the example application of tele-operation.Comment: 8 pages, 8 figures, technical repor
Realistic Human Motion Preservation-Imitation Development on Robot with Kinect
At most, motion generation on robot is usually done through complex computation in off-line mode and straightforward method. In straightforward method, the operator drives robot to certain pose either with moving manipulator tool-tip with hand or remotely. Once the desired pose achieved, the current pose is saved to memory. However, these methods are time consuming. An easy and quick approach is by imitating an object motion to robot with sensing devices. There have been numerous efforts for motion imitation either by using position sensitive detector (PSD) or by using stereo camera. However, a calibrated pre-condition should be done initially, which is not possible for natural movement. Here, this paper proposed motion preservation by capturing human motion naturally through Kinect and then reproduced human motion on humanoid robot simultaneously. In addition, the motions are also preserved in database for later used on robot motion generation and teaching as well. Furthermore, the robot motions are developed to run smoothly and close to human eye ability. The proposed method has been validated in experimental results by capturing and reproducing human motion on robot in rate of 20Hz with340us computation cost for each process
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