4 research outputs found
Robust, Compliant Assembly via Optimal Belief Space Planning
In automated manufacturing, robots must reliably assemble parts of various
geometries and low tolerances. Ideally, they plan the required motions
autonomously. This poses a substantial challenge due to high-dimensional state
spaces and non-linear contact-dynamics. Furthermore, object poses and model
parameters, such as friction, are not exactly known and a source of
uncertainty. The method proposed in this paper models the task of parts
assembly as a belief space planning problem over an underlying
impedance-controlled, compliant system. To solve this planning problem we
introduce an asymptotically optimal belief space planner by extending an
optimal, randomized, kinodynamic motion planner to non-deterministic domains.
Under an expansiveness assumption we establish probabilistic completeness and
asymptotic optimality. We validate our approach in thorough, simulated and
real-world experiments of multiple assembly tasks. The experiments demonstrate
our planner's ability to reliably assemble objects, solely based on CAD models
as input.Comment: 8 page
Pushing Fast and Slow: Task-Adaptive Planning for Non-prehensile Manipulation Under Uncertainty
We propose a planning and control approach to physics-based manipulation. The
key feature of the algorithm is that it can adapt to the accuracy requirements
of a task, by slowing down and generating `careful' motion when the task
requires high accuracy, and by speeding up and moving fast when the task
tolerates inaccuracy. We formulate the problem as an MDP with action-dependent
stochasticity and propose an approximate online solution to it. We use a
trajectory optimizer with a deterministic model to suggest promising actions to
the MDP, to reduce computation time spent on evaluating different actions. We
conducted experiments in simulation and on a real robotic system. Our results
show that with a task-adaptive planning and control approach, a robot can
choose fast or slow actions depending on the task accuracy and uncertainty
level. The robot makes these decisions online and is able to maintain high
success rates while completing manipulation tasks as fast as possible.Comment: Camera-ready manuscript for WAFR 201
A probabilistic framework for tracking uncertainties in robotic manipulation
Precisely tracking uncertainties is crucial for robots to successfully and
safely operate in unstructured and dynamic environments. We present a
probabilistic framework to precisely keep track of uncertainties throughout the
entire manipulation process. In agreement with common manipulation pipelines,
we decompose the process into two subsequent stages, namely perception and
physical interaction. Each stage is associated with different sources and types
of uncertainties, requiring different techniques. We discuss which
representation of uncertainties is the most appropriate for each stage (e.g. as
probability distributions in SE(3) during perception, as weighted particles
during physical interactions), how to convert from one representation to
another, and how to initialize or update the uncertainties at each step of the
process (camera calibration, image processing, pushing, grasping, etc.).
Finally, we demonstrate the benefit of this fine-grained knowledge of
uncertainties in an actual assembly task.Comment: 7 pages, 4 figure
Manipulation with Shared Grasping
A shared grasp is a grasp formed by contacts between the manipulated object
and both the robot hand and the environment. By trading off hand contacts for
environmental contacts, a shared grasp requires fewer contacts with the hand,
and enables manipulation even when a full grasp is not possible. Previous
research has used shared grasps for non-prehensile manipulation such as
pivoting and tumbling. This paper treats the problem more generally, with
methods to select the best shared grasp and robot actions for a desired object
motion. The central issue is to evaluate the feasible contact modes: for each
contact, whether that contact will remain active, and whether slip will occur.
Robustness is important. When a contact mode fails, e.g., when a contact is
lost, or when unintentional slip occurs, the operation will fail, and in some
cases damage may occur. In this work, we enumerate all feasible contact modes,
calculate corresponding controls, and select the most robust candidate. We can
also optimize the contact geometry for robustness. This paper employs
quasi-static analysis of planar rigid bodies with Coulomb friction to derive
the algorithms and controls. Finally, we demonstrate the robustness of shared
grasping and the use of our methods in representative experiments and examples.
The video can be found at https://youtu.be/tyNhJvRYZN