68 research outputs found
Experimental evaluation of synergy-based in-hand manipulation
In this paper, the problem of in-hand dexterous manipulation has been addressed on the base
of postural synergies analysis. The computation of the synergies subspace able to represent grasp and
manipulation tasks as trajectories connecting suitable configuration sets is based on the observation of
the human hand behavior. Five subjects are required to reproduce themost natural grasping configuration
belonging to the considered grasping taxonomy and the boundary configurations for those grasps that
admit internal manipulation. The measurements on the human hand and the reconstruction of the human
grasp configurations are obtained using a vision-based mapping method that assume the kinematics
of the robotic hand, used for the experiments, as a simplified model of the human hand. The analysis
to determine the most suitable set of synergies able to reproduce the selected grasps and the relative
allowed internal manipulation has been carried out. The grasping and in-hand manipulation tasks have
been reproduced bymeans of linear interpolation of the boundary configurations in the selected synergies
subspace and the results have been experimentally tested on the UB Hand IV
Impedence Control for Variable Stiffness Mechanisms with Nonlinear Joint Coupling
The current discussion on physical human robot
interaction and the related safety aspects, but also the interest
of neuro-scientists to validate their hypotheses on human motor
skills with bio-mimetic robots, led to a recent revival of tendondriven
robots. In this paper, the modeling of tendon-driven
elastic systems with nonlinear couplings is recapitulated. A
control law is developed that takes the desired joint position
and stiffness as input. Therefore, desired motor positions are
determined that are commanded to an impedance controller.
We give a physical interpretation of the controller. More importantly,
a static decoupling of the joint motion and the stiffness
variation is given. The combination of active (controller) and
passive (mechanical) stiffness is investigated. The controller
stiffness is designed according to the desired overall stiffness.
A damping design of the impedance controller is included in
these considerations. The controller performance is evaluated
in simulation
A stochastic state estimation approach to simultaneous localization and map building
This monograph covers theoretical aspects of simultaneous localization and map building for mobile robots, such as estimation stability, nonlinear models for the propagation of uncertainties, temporal landmark compatibility, as well as issues pertaining the coupling of control and SLAM. One of the most relevant topics covered in this monograph is the theoretical formalism of partial observability in SLAM. The authors show that the typical approach to SLAM using a Kalman filter results in marginal filter stability, making the final reconstruction estimates dependent on the initial vehicle estimates. However, by anchoring the map to a fixed landmark in the scene, they are able to attain full observability in SLAM, with reduced covariance estimates. This result earned the first author the EURON Georges Giralt Best PhD Award in its fourth edition, and has prompted the SLAM community to think in new ways to approach the mapping problem. For example, by creating local maps anchored on a landmark, or on the robot initial estimate itself, and then using geometric relations to fuse local maps globally. This monograph is appropriate as a text for an introductory estimation-theoretic approach to the SLAM problem, and as a reference book for people who work in mobile robotics research in general.This work was supported by projects: 'Supervised learning of industrial scenes by means of an active vision equipped mobile robot.' (J-00063), 'Integration of robust perception, learning, and navigation systems in mobile robotics' (J-0929).Peer Reviewe
The Operation of Autonomous Mobile Robot Assistants in the Environment of Care Facilities Adopting a User-Centered Development Design
The successful development of autonomous mobile robot assistants depends significantly on the well-balanced reconcilements of the technically possible and the socially desirable. Based on empirical research 2 substantiated conclusions can be established for the suitability of "scenario-based design" (Rosson/Carroll 2003) for the successful development of mobile robot assistants and automated guided vehicles to be applied for service functions in stationary care facilities for seniors.User-Centered Technology Development, Knowledge-Transfer, Participative Assessment Methods, Robotics
Gaussian-Process-based Robot Learning from Demonstration
Endowed with higher levels of autonomy, robots are required to perform
increasingly complex manipulation tasks. Learning from demonstration is arising
as a promising paradigm for transferring skills to robots. It allows to
implicitly learn task constraints from observing the motion executed by a human
teacher, which can enable adaptive behavior. We present a novel
Gaussian-Process-based learning from demonstration approach. This probabilistic
representation allows to generalize over multiple demonstrations, and encode
variability along the different phases of the task. In this paper, we address
how Gaussian Processes can be used to effectively learn a policy from
trajectories in task space. We also present a method to efficiently adapt the
policy to fulfill new requirements, and to modulate the robot behavior as a
function of task variability. This approach is illustrated through a real-world
application using the TIAGo robot.Comment: 8 pages, 10 figure
Path Planning for Simple Robots using Soft Subdivision Search
The concept of resolution-exact path planning is a theoretically sound alternative to the standard exact algorithms, and provides much stronger guarantees than probabilistic or sampling algorithms. It opens the way for the introduction of soft predicates in the context of subdivision algorithm. Taking a leaf from the great success of the Probabilistic Road Map (PRM) framework, we formulate an analogous framework for subdivision, called Soft Subdivision Search (SSS). In this video, we illustrate the SSS framework for a trio of simple planar robots: disc, triangle and 2-links. These robots have, respectively, 2, 3 and 4 degrees of freedom. Our 2-link robot can also avoid self-crossing. These algorithms operate in realtime and are relatively easy to implement
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