25,092 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
STAP: Sequencing Task-Agnostic Policies
Advances in robotic skill acquisition have made it possible to build
general-purpose libraries of learned skills for downstream manipulation tasks.
However, naively executing these skills one after the other is unlikely to
succeed without accounting for dependencies between actions prevalent in
long-horizon plans. We present Sequencing Task-Agnostic Policies (STAP), a
scalable framework for training manipulation skills and coordinating their
geometric dependencies at planning time to solve long-horizon tasks never seen
by any skill during training. Given that Q-functions encode a measure of skill
feasibility, we formulate an optimization problem to maximize the joint success
of all skills sequenced in a plan, which we estimate by the product of their
Q-values. Our experiments indicate that this objective function approximates
ground truth plan feasibility and, when used as a planning objective, reduces
myopic behavior and thereby promotes long-horizon task success. We further
demonstrate how STAP can be used for task and motion planning by estimating the
geometric feasibility of skill sequences provided by a task planner. We
evaluate our approach in simulation and on a real robot. Qualitative results
and code are made available at https://sites.google.com/stanford.edu/stap/home
Contingent task and motion planning under uncertainty for human–robot interactions
Manipulation planning under incomplete information is a highly challenging task for mobile manipulators. Uncertainty can be resolved by robot perception modules or using human knowledge in the execution process. Human operators can also collaborate with robots for the execution of some difficult actions or as helpers in sharing the task knowledge. In this scope, a contingent-based task and motion planning is proposed taking into account robot uncertainty and human–robot interactions, resulting a tree-shaped set of geometrically feasible plans. Different sorts of geometric reasoning processes are embedded inside the planner to cope with task constraints like detecting occluding objects when a robot needs to grasp an object. The proposal has been evaluated with different challenging scenarios in simulation and a real environment.Postprint (published version
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