283,604 research outputs found
Learning Contact-Rich Manipulation Skills with Guided Policy Search
Autonomous learning of object manipulation skills can enable robots to
acquire rich behavioral repertoires that scale to the variety of objects found
in the real world. However, current motion skill learning methods typically
restrict the behavior to a compact, low-dimensional representation, limiting
its expressiveness and generality. In this paper, we extend a recently
developed policy search method \cite{la-lnnpg-14} and use it to learn a range
of dynamic manipulation behaviors with highly general policy representations,
without using known models or example demonstrations. Our approach learns a set
of trajectories for the desired motion skill by using iteratively refitted
time-varying linear models, and then unifies these trajectories into a single
control policy that can generalize to new situations. To enable this method to
run on a real robot, we introduce several improvements that reduce the sample
count and automate parameter selection. We show that our method can acquire
fast, fluent behaviors after only minutes of interaction time, and can learn
robust controllers for complex tasks, including putting together a toy
airplane, stacking tight-fitting lego blocks, placing wooden rings onto
tight-fitting pegs, inserting a shoe tree into a shoe, and screwing bottle caps
onto bottles
Formula manipulation in the bond graph modelling and simulation of large mechanical systems
A multibond graph element for a general single moving body is derived. A multibody system can easily be described as an interconnection of these elements. 3-D mechanical systems usually contain dependent inertias having both differential and integral causality. A method is described for the transformation of inertias with differential causality to an integral form, using formula manipulation. The program also helps to find experimentally the optimal choice for the generalized coordinates. The resulting explicit differential equation may be solved using a standard integration routine or simulation program
Robotic manipulation of a rotating chain
This paper considers the problem of manipulating a uniformly rotating chain:
the chain is rotated at a constant angular speed around a fixed axis using a
robotic manipulator. Manipulation is quasi-static in the sense that transitions
are slow enough for the chain to be always in "rotational" equilibrium. The
curve traced by the chain in a rotating plane -- its shape function -- can be
determined by a simple force analysis, yet it possesses complex multi-solutions
behavior typical of non-linear systems. We prove that the configuration space
of the uniformly rotating chain is homeomorphic to a two-dimensional surface
embedded in . Using that representation, we devise a manipulation
strategy for transiting between different rotation modes in a stable and
controlled manner. We demonstrate the strategy on a physical robotic arm
manipulating a rotating chain. Finally, we discuss how the ideas developed here
might find fruitful applications in the study of other flexible objects, such
as elastic rods or concentric tubes.Comment: 12 pages, 9 figure
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
- …