154 research outputs found
A Single-Query Manipulation Planner
In manipulation tasks, a robot interacts with movable object(s). The
configuration space in manipulation planning is thus the Cartesian product of
the configuration space of the robot with those of the movable objects. It is
the complex structure of such a "Composite Configuration Space" that makes
manipulation planning particularly challenging. Previous works approximate the
connectivity of the Composite Configuration Space by means of discretization or
by creating random roadmaps. Such approaches involve an extensive
pre-processing phase, which furthermore has to be re-done each time the
environment changes. In this paper, we propose a high-level Grasp-Placement
Table similar to that proposed by Tournassoud et al. (1987), but which does not
require any discretization or heavy pre-processing. The table captures the
potential connectivity of the Composite Configuration Space while being
specific only to the movable object: in particular, it does not require to be
re-computed when the environment changes. During the query phase, the table is
used to guide a tree-based planner that explores the space systematically. Our
simulations and experiments show that the proposed method enables improvements
in both running time and trajectory quality as compared to existing approaches.Comment: 8 pages, 7 figures, 1 tabl
Pick and Place Without Geometric Object Models
We propose a novel formulation of robotic pick and place as a deep
reinforcement learning (RL) problem. Whereas most deep RL approaches to robotic
manipulation frame the problem in terms of low level states and actions, we
propose a more abstract formulation. In this formulation, actions are target
reach poses for the hand and states are a history of such reaches. We show this
approach can solve a challenging class of pick-place and regrasping problems
where the exact geometry of the objects to be handled is unknown. The only
information our method requires is: 1) the sensor perception available to the
robot at test time; 2) prior knowledge of the general class of objects for
which the system was trained. We evaluate our method using objects belonging to
two different categories, mugs and bottles, both in simulation and on real
hardware. Results show a major improvement relative to a shape primitives
baseline
A Certified-Complete Bimanual Manipulation Planner
Planning motions for two robot arms to move an object collaboratively is a
difficult problem, mainly because of the closed-chain constraint, which arises
whenever two robot hands simultaneously grasp a single rigid object. In this
paper, we propose a manipulation planning algorithm to bring an object from an
initial stable placement (position and orientation of the object on the support
surface) towards a goal stable placement. The key specificity of our algorithm
is that it is certified-complete: for a given object and a given environment,
we provide a certificate that the algorithm will find a solution to any
bimanual manipulation query in that environment whenever one exists. Moreover,
the certificate is constructive: at run-time, it can be used to quickly find a
solution to a given query. The algorithm is tested in software and hardware on
a number of large pieces of furniture.Comment: 12 pages, 7 figures, 1 tabl
Using automatic robot programming for space telerobotics
The interpreter of a task level robot programming system called Handey is described. Handey is a system that can recognize, manipulate and assemble polyhedral parts when given only a specification of the goal. To perform an assembly, Handey makes use of a recognition module, a gross motion planner, a grasp planner, a local approach planner and is capable of planning part re-orientation. The possibility of including these modules in a telerobotics work-station is discussed
- …