2 research outputs found
Approximate Inference-based Motion Planning by Learning and Exploiting Low-Dimensional Latent Variable Models
This work presents an efficient framework to generate a motion plan of a
robot with high degrees of freedom (e.g., a humanoid robot).
High-dimensionality of the robot configuration space often leads to
difficulties in utilizing the widely-used motion planning algorithms, since the
volume of the decision space increases exponentially with the number of
dimensions. To handle complications arising from the large decision space, and
to solve a corresponding motion planning problem efficiently, two key concepts
are adopted in this work: First, the Gaussian process latent variable model
(GP-LVM) is utilized for low-dimensional representation of the original
configuration space. Second, an approximate inference algorithm is used,
exploiting through the duality between control and estimation, to explore the
decision space and to compute a high-quality motion trajectory of the robot.
Utilizing the GP-LVM and the duality between control and estimation, we
construct a fully probabilistic generative model with which a high-dimensional
motion planning problem is transformed into a tractable inference problem.
Finally, we compute the motion trajectory via an approximate inference
algorithm based on a variant of the particle filter. The resulting motions can
be viewed in the supplemental video. ( https://youtu.be/kngEaOR4Esc )Comment: Accepted for publication in IEEE Robotics and Automation Letters
(RA-L), 201
Probabilistic Framework for Constrained Manipulations and Task and Motion Planning under Uncertainty
Logic-Geometric Programming (LGP) is a powerful motion and manipulation
planning framework, which represents hierarchical structure using logic rules
that describe discrete aspects of problems, e.g., touch, grasp, hit, or push,
and solves the resulting smooth trajectory optimization. The expressive power
of logic allows LGP for handling complex, large-scale sequential manipulation
and tool-use planning problems. In this paper, we extend the LGP formulation to
stochastic domains. Based on the control-inference duality, we interpret LGP in
a stochastic domain as fitting a mixture of Gaussians to the posterior path
distribution, where each logic profile defines a single Gaussian path
distribution. The proposed framework enables a robot to prioritize various
interaction modes and to acquire interesting behaviors such as contact
exploitation for uncertainty reduction, eventually providing a composite
control scheme that is reactive to disturbance. The supplementary video can be
found at https://youtu.be/CEaJdVlSZyoComment: ICRA 202