136,908 research outputs found
Asymmetric Dual-Arm Task Execution using an Extended Relative Jacobian
Coordinated dual-arm manipulation tasks can be broadly characterized as
possessing absolute and relative motion components. Relative motion tasks, in
particular, are inherently redundant in the way they can be distributed between
end-effectors. In this work, we analyse cooperative manipulation in terms of
the asymmetric resolution of relative motion tasks. We discuss how existing
approaches enable the asymmetric execution of a relative motion task, and show
how an asymmetric relative motion space can be defined. We leverage this result
to propose an extended relative Jacobian to model the cooperative system, which
allows a user to set a concrete degree of asymmetry in the task execution. This
is achieved without the need for prescribing an absolute motion target.
Instead, the absolute motion remains available as a functional redundancy to
the system. We illustrate the properties of our proposed Jacobian through
numerical simulations of a novel differential Inverse Kinematics algorithm.Comment: Accepted for presentation at ISRR19. 16 Page
Analyzing Whole-Body Pose Transitions in Multi-Contact Motions
When executing whole-body motions, humans are able to use a large variety of
support poses which not only utilize the feet, but also hands, knees and elbows
to enhance stability. While there are many works analyzing the transitions
involved in walking, very few works analyze human motion where more complex
supports occur.
In this work, we analyze complex support pose transitions in human motion
involving locomotion and manipulation tasks (loco-manipulation). We have
applied a method for the detection of human support contacts from motion
capture data to a large-scale dataset of loco-manipulation motions involving
multi-contact supports, providing a semantic representation of them. Our
results provide a statistical analysis of the used support poses, their
transitions and the time spent in each of them. In addition, our data partially
validates our taxonomy of whole-body support poses presented in our previous
work.
We believe that this work extends our understanding of human motion for
humanoids, with a long-term objective of developing methods for autonomous
multi-contact motion planning.Comment: 8 pages, IEEE-RAS International Conference on Humanoid Robots
(Humanoids) 201
Enabling Motion Planning and Execution for Tasks Involving Deformation and Uncertainty
A number of outstanding problems in robotic motion and manipulation involve tasks where degrees of freedom (DoF), be they part of the robot, an object being manipulated, or the surrounding environment, cannot be accurately controlled by the actuators of the robot alone. Rather, they are also controlled by physical properties or interactions - contact, robot dynamics, actuator behavior - that are influenced by the actuators of the robot. In particular, we focus on two important areas of poorly controlled robotic manipulation: motion planning for deformable objects and in deformable environments; and manipulation with uncertainty. Many everyday tasks we wish robots to perform, such as cooking and cleaning, require the robot to manipulate deformable objects. The limitations of real robotic actuators and sensors result in uncertainty that we must address to reliably perform fine manipulation. Notably, both areas share a common principle: contact, which is usually prohibited in motion planners, is not only sometimes unavoidable, but often necessary to accurately complete the task at hand. We make four contributions that enable robot manipulation in these poorly controlled tasks: First, an efficient discretized representation of elastic deformable objects and cost function that assess a ``cost of deformation\u27 for a specific configuration of a deformable object that enables deformable object manipulation tasks to be performed without physical simulation. Second, a method using active learning and inverse-optimal control to build these discretized representations from expert demonstrations. Third, a motion planner and policy-based execution approach to manipulation with uncertainty which incorporates contact with the environment and compliance of the robot to generate motion policies which are then adapted during execution to reflect actual robot behavior. Fourth, work towards the development of an efficient path quality metric for paths executed with actuation uncertainty that can be used inside a motion planner or trajectory optimizer
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