1,503 research outputs found
Unsupervised Contact Learning for Humanoid Estimation and Control
This work presents a method for contact state estimation using fuzzy
clustering to learn contact probability for full, six-dimensional humanoid
contacts. The data required for training is solely from proprioceptive sensors
- endeffector contact wrench sensors and inertial measurement units (IMUs) -
and the method is completely unsupervised. The resulting cluster means are used
to efficiently compute the probability of contact in each of the six
endeffector degrees of freedom (DoFs) independently. This clustering-based
contact probability estimator is validated in a kinematics-based base state
estimator in a simulation environment with realistic added sensor noise for
locomotion over rough, low-friction terrain on which the robot is subject to
foot slip and rotation. The proposed base state estimator which utilizes these
six DoF contact probability estimates is shown to perform considerably better
than that which determines kinematic contact constraints purely based on
measured normal force.Comment: Submitted to the IEEE International Conference on Robotics and
Automation (ICRA) 201
Unsupervised Contact Learning for Humanoid Estimation and Control
This work presents a method for contact state estimation using fuzzy
clustering to learn contact probability for full, six-dimensional humanoid
contacts. The data required for training is solely from proprioceptive sensors
- endeffector contact wrench sensors and inertial measurement units (IMUs) -
and the method is completely unsupervised. The resulting cluster means are used
to efficiently compute the probability of contact in each of the six
endeffector degrees of freedom (DoFs) independently. This clustering-based
contact probability estimator is validated in a kinematics-based base state
estimator in a simulation environment with realistic added sensor noise for
locomotion over rough, low-friction terrain on which the robot is subject to
foot slip and rotation. The proposed base state estimator which utilizes these
six DoF contact probability estimates is shown to perform considerably better
than that which determines kinematic contact constraints purely based on
measured normal force.Comment: Submitted to the IEEE International Conference on Robotics and
Automation (ICRA) 201
Using humanoid robots to study human behavior
Our understanding of human behavior advances as our humanoid robotics work progresses-and vice versa. This team's work focuses on trajectory formation and planning, learning from demonstration, oculomotor control and interactive behaviors. They are programming robotic behavior based on how we humans âprogramâ behavior in-or train-each other
Unsupervised human-to-robot motion retargeting via expressive latent space
This paper introduces a novel approach for human-to-robot motion retargeting,
enabling robots to mimic human motion with precision while preserving the
semantics of the motion. For that, we propose a deep learning method for direct
translation from human to robot motion. Our method does not require annotated
paired human-to-robot motion data, which reduces the effort when adopting new
robots. To this end, we first propose a cross-domain similarity metric to
compare the poses from different domains (i.e., human and robot). Then, our
method achieves the construction of a shared latent space via contrastive
learning and decodes latent representations to robot motion control commands.
The learned latent space exhibits expressiveness as it captures the motions
precisely and allows direct motion control in the latent space. We showcase how
to generate in-between motion through simple linear interpolation in the latent
space between two projected human poses. Additionally, we conducted a
comprehensive evaluation of robot control using diverse modality inputs, such
as texts, RGB videos, and key-poses, which enhances the ease of robot control
to users of all backgrounds. Finally, we compare our model with existing works
and quantitatively and qualitatively demonstrate the effectiveness of our
approach, enhancing natural human-robot communication and fostering trust in
integrating robots into daily life
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