3,291 research outputs found
A Whole-Body Pose Taxonomy for Loco-Manipulation Tasks
Exploiting interaction with the environment is a promising and powerful way
to enhance stability of humanoid robots and robustness while executing
locomotion and manipulation tasks. Recently some works have started to show
advances in this direction considering humanoid locomotion with multi-contacts,
but to be able to fully develop such abilities in a more autonomous way, we
need to first understand and classify the variety of possible poses a humanoid
robot can achieve to balance. To this end, we propose the adaptation of a
successful idea widely used in the field of robot grasping to the field of
humanoid balance with multi-contacts: a whole-body pose taxonomy classifying
the set of whole-body robot configurations that use the environment to enhance
stability. We have revised criteria of classification used to develop grasping
taxonomies, focusing on structuring and simplifying the large number of
possible poses the human body can adopt. We propose a taxonomy with 46 poses,
containing three main categories, considering number and type of supports as
well as possible transitions between poses. The taxonomy induces a
classification of motion primitives based on the pose used for support, and a
set of rules to store and generate new motions. We present preliminary results
that apply known segmentation techniques to motion data from the KIT whole-body
motion database. Using motion capture data with multi-contacts, we can identify
support poses providing a segmentation that can distinguish between locomotion
and manipulation parts of an action.Comment: 8 pages, 7 figures, 1 table with full page figure that appears in
landscape page, 2015 IEEE/RSJ International Conference on Intelligent Robots
and System
Learning Task Priorities from Demonstrations
Bimanual operations in humanoids offer the possibility to carry out more than
one manipulation task at the same time, which in turn introduces the problem of
task prioritization. We address this problem from a learning from demonstration
perspective, by extending the Task-Parameterized Gaussian Mixture Model
(TP-GMM) to Jacobian and null space structures. The proposed approach is tested
on bimanual skills but can be applied in any scenario where the prioritization
between potentially conflicting tasks needs to be learned. We evaluate the
proposed framework in: two different tasks with humanoids requiring the
learning of priorities and a loco-manipulation scenario, showing that the
approach can be exploited to learn the prioritization of multiple tasks in
parallel.Comment: Accepted for publication at the IEEE Transactions on Robotic
Humanoid robot walking control on inclined planes
The humanoid bipedal structure is suitable for a assitive robot functioning in the human environment. However, the bipedal walk is a difficult control problem. Walking just on even floor is not satisfactory for the applicability of a humanoid robot. This paper presents a study on bipedal walk on inclined planes. A Zero Moment Point (ZMP) based reference generation technique is employed. The orientation of the feet is adjusted online by a fuzzy logic system to adapt to different walking surface slopes. This system uses a sampling time larger than the one of the joint space position controllers. The average value of the body pitch angle is used as the inputs to the fuzzy logic system. A foot pitch orientation compensator implemented independently for the two feet complements the fuzyy controller. A 12-degrees-of-freedom (DOF) biped robot model is used in the full-dynamics 3-D simulations. Simulations are carried out on even floor and inclined planes with different slopes. The results indicate that the control method presented is successful in enabling the robot to climb slopes of 8.5 degrees (15 percent grade)
3LP: a linear 3D-walking model including torso and swing dynamics
In this paper, we present a new model of biped locomotion which is composed
of three linear pendulums (one per leg and one for the whole upper body) to
describe stance, swing and torso dynamics. In addition to double support, this
model has different actuation possibilities in the swing hip and stance ankle
which could be widely used to produce different walking gaits. Without the need
for numerical time-integration, closed-form solutions help finding periodic
gaits which could be simply scaled in certain dimensions to modulate the motion
online. Thanks to linearity properties, the proposed model can provide a
computationally fast platform for model predictive controllers to predict the
future and consider meaningful inequality constraints to ensure feasibility of
the motion. Such property is coming from describing dynamics with joint torques
directly and therefore, reflecting hardware limitations more precisely, even in
the very abstract high level template space. The proposed model produces
human-like torque and ground reaction force profiles and thus, compared to
point-mass models, it is more promising for precise control of humanoid robots.
Despite being linear and lacking many other features of human walking like CoM
excursion, knee flexion and ground clearance, we show that the proposed model
can predict one of the main optimality trends in human walking, i.e. nonlinear
speed-frequency relationship. In this paper, we mainly focus on describing the
model and its capabilities, comparing it with human data and calculating
optimal human gait variables. Setting up control problems and advanced
biomechanical analysis still remain for future works.Comment: Journal paper under revie
Markerless visual servoing on unknown objects for humanoid robot platforms
To precisely reach for an object with a humanoid robot, it is of central
importance to have good knowledge of both end-effector, object pose and shape.
In this work we propose a framework for markerless visual servoing on unknown
objects, which is divided in four main parts: I) a least-squares minimization
problem is formulated to find the volume of the object graspable by the robot's
hand using its stereo vision; II) a recursive Bayesian filtering technique,
based on Sequential Monte Carlo (SMC) filtering, estimates the 6D pose
(position and orientation) of the robot's end-effector without the use of
markers; III) a nonlinear constrained optimization problem is formulated to
compute the desired graspable pose about the object; IV) an image-based visual
servo control commands the robot's end-effector toward the desired pose. We
demonstrate effectiveness and robustness of our approach with extensive
experiments on the iCub humanoid robot platform, achieving real-time
computation, smooth trajectories and sub-pixel precisions
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