152 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
Learning Ground Traversability from Simulations
Mobile ground robots operating on unstructured terrain must predict which
areas of the environment they are able to pass in order to plan feasible paths.
We address traversability estimation as a heightmap classification problem: we
build a convolutional neural network that, given an image representing the
heightmap of a terrain patch, predicts whether the robot will be able to
traverse such patch from left to right. The classifier is trained for a
specific robot model (wheeled, tracked, legged, snake-like) using simulation
data on procedurally generated training terrains; the trained classifier can be
applied to unseen large heightmaps to yield oriented traversability maps, and
then plan traversable paths. We extensively evaluate the approach in simulation
on six real-world elevation datasets, and run a real-robot validation in one
indoor and one outdoor environment.Comment: Webpage: http://romarcg.xyz/traversability_estimation
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
Preintegrated Velocity Bias Estimation to Overcome Contact Nonlinearities in Legged Robot Odometry
In this paper, we present a novel factor graph formulation to estimate the
pose and velocity of a quadruped robot on slippery and deformable terrain. The
factor graph introduces a preintegrated velocity factor that incorporates
velocity inputs from leg odometry and also estimates related biases. From our
experimentation we have seen that it is difficult to model uncertainties at the
contact point such as slip or deforming terrain, as well as leg flexibility. To
accommodate for these effects and to minimize leg odometry drift, we extend the
robot's state vector with a bias term for this preintegrated velocity factor.
The bias term can be accurately estimated thanks to the tight fusion of the
preintegrated velocity factor with stereo vision and IMU factors, without which
it would be unobservable. The system has been validated on several scenarios
that involve dynamic motions of the ANYmal robot on loose rocks, slopes and
muddy ground. We demonstrate a 26% improvement of relative pose error compared
to our previous work and 52% compared to a state-of-the-art proprioceptive
state estimator.Comment: Accepted to ICRA 2020. Video: youtu.be/w1Sx6dIqgQ
ProNav: Proprioceptive Traversability Estimation for Legged Robot Navigation in Outdoor Environments
We propose a novel method, ProNav, which uses proprioceptive signals for
traversability estimation in challenging outdoor terrains for autonomous legged
robot navigation. Our approach uses sensor data from a legged robot's joint
encoders, force, and current sensors to measure the joint positions, forces,
and current consumption respectively to accurately assess a terrain's
stability, resistance to the robot's motion, risk of entrapment, and crash.
Based on these factors, we compute the appropriate robot gait to maximize
stability, which leads to reduced energy consumption. Our approach can also be
used to predict imminent crashes in challenging terrains and execute behaviors
to preemptively avoid them. We integrate ProNav with an exteroceptive-based
method to navigate real-world environments with dense vegetation, high
granularity, negative obstacles, etc. Our method shows an improvement up to 40%
in terms of success rate and up to 15.1% reduction in terms of energy
consumption compared to exteroceptive-based methods
Legged locomotion over irregular terrains: State of the art of human and robot performance
Legged robotic technologies have moved out of the lab to operate in real environments, characterized by a wide variety of unpredictable irregularities and disturbances, all this in close proximity with humans. Demonstrating the ability of current robots to move robustly and reliably in these conditions is becoming essential to prove their safe operation. Here, we report an in-depth literature review aimed at verifying the existence of common or agreed protocols and metrics to test the performance of legged system in realistic environments. We primarily focused on three types of robotic technologies, i.e., hexapods, quadrupeds and bipeds. We also included a comprehensive overview on human locomotion studies, being it often considered the gold standard for performance, and one of the most important sources of bioinspiration for legged machines. We discovered that very few papers have rigorously studied robotic locomotion under irregular terrain conditions. On the contrary, numerous studies have addressed this problem on human gait, being nonetheless of highly heterogeneous nature in terms of experimental design. This lack of agreed methodology makes it challenging for the community to properly assess, compare and predict the performance of existing legged systems in real environments. On the one hand, this work provides a library of methods, metrics and experimental protocols, with a critical analysis on the limitations of the current approaches and future promising directions. On the other hand, it demonstrates the existence of an important lack of benchmarks in the literature, and the possibility of bridging different disciplines, e.g., the human and robotic, towards the definition of standardized procedure that will boost not only the scientific development of better bioinspired solutions, but also their market uptake
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