10,642 research outputs found
Reactive Planar Manipulation with Convex Hybrid MPC
This paper presents a reactive controller for planar manipulation tasks that
leverages machine learning to achieve real-time performance. The approach is
based on a Model Predictive Control (MPC) formulation, where the goal is to
find an optimal sequence of robot motions to achieve a desired object motion.
Due to the multiple contact modes associated with frictional interactions, the
resulting optimization program suffers from combinatorial complexity when
tasked with determining the optimal sequence of modes.
To overcome this difficulty, we formulate the search for the optimal mode
sequences offline, separately from the search for optimal control inputs
online. Using tools from machine learning, this leads to a convex hybrid MPC
program that can be solved in real-time. We validate our algorithm on a planar
manipulation experimental setup where results show that the convex hybrid MPC
formulation with learned modes achieves good closed-loop performance on a
trajectory tracking problem
Stable Prehensile Pushing: In-Hand Manipulation with Alternating Sticking Contacts
This paper presents an approach to in-hand manipulation planning that
exploits the mechanics of alternating sticking contact. Particularly, we
consider the problem of manipulating a grasped object using external pushes for
which the pusher sticks to the object. Given the physical properties of the
object, frictional coefficients at contacts and a desired regrasp on the
object, we propose a sampling-based planning framework that builds a pushing
strategy concatenating different feasible stable pushes to achieve the desired
regrasp. An efficient dynamics formulation allows us to plan in-hand
manipulations 100-1000 times faster than our previous work which builds upon a
complementarity formulation. Experimental observations for the generated plans
show that the object precisely moves in the grasp as expected by the planner.
Video Summary -- youtu.be/qOTKRJMx6HoComment: IEEE International Conference on Robotics and Automation 201
Real-Time Online Re-Planning for Grasping Under Clutter and Uncertainty
We consider the problem of grasping in clutter. While there have been motion
planners developed to address this problem in recent years, these planners are
mostly tailored for open-loop execution. Open-loop execution in this domain,
however, is likely to fail, since it is not possible to model the dynamics of
the multi-body multi-contact physical system with enough accuracy, neither is
it reasonable to expect robots to know the exact physical properties of
objects, such as frictional, inertial, and geometrical. Therefore, we propose
an online re-planning approach for grasping through clutter. The main challenge
is the long planning times this domain requires, which makes fast re-planning
and fluent execution difficult to realize. In order to address this, we propose
an easily parallelizable stochastic trajectory optimization based algorithm
that generates a sequence of optimal controls. We show that by running this
optimizer only for a small number of iterations, it is possible to perform real
time re-planning cycles to achieve reactive manipulation under clutter and
uncertainty.Comment: Published as a conference paper in IEEE Humanoids 201
Dexterous Manipulation Graphs
We propose the Dexterous Manipulation Graph as a tool to address in-hand
manipulation and reposition an object inside a robot's end-effector. This graph
is used to plan a sequence of manipulation primitives so to bring the object to
the desired end pose. This sequence of primitives is translated into motions of
the robot to move the object held by the end-effector. We use a dual arm robot
with parallel grippers to test our method on a real system and show successful
planning and execution of in-hand manipulation
The kinematics of hyper-redundant robot locomotion
This paper considers the kinematics of hyper-redundant (or “serpentine”) robot locomotion over uneven solid terrain, and presents algorithms to implement a variety of “gaits”. The analysis and algorithms are based on a continuous backbone curve model which captures the robot's macroscopic geometry. Two classes of gaits, based on stationary waves and traveling waves of mechanism deformation, are introduced for hyper-redundant robots of both constant and variable length. We also illustrate how the locomotion algorithms can be used to plan the manipulation of objects which are grasped in a tentacle-like manner. Several of these gaits and the manipulation algorithm have been implemented on a 30 degree-of-freedom hyper-redundant robot. Experimental results are presented to demonstrate and validate these concepts and our modeling assumptions
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