30 research outputs found
Completely Mixed Stochastic Games with Small Unfixed Discount Factor
International audienceMotivated by uncertainty in the value of the interest rate, we study discounted zero-sum stochastic games with unfixed discount factor. Our general goal is to obtain a power series expansion of the value of the game with respect to the discount factor around its nominal value. We consider a specific but important class of stochastic games – completely mixed stochastic games. As an illustrative example we take tax evasion model
Learning Geometric Representations of Objects via Interaction
We address the problem of learning representations from observations of a
scene involving an agent and an external object the agent interacts with. To
this end, we propose a representation learning framework extracting the
location in physical space of both the agent and the object from unstructured
observations of arbitrary nature. Our framework relies on the actions performed
by the agent as the only source of supervision, while assuming that the object
is displaced by the agent via unknown dynamics. We provide a theoretical
foundation and formally prove that an ideal learner is guaranteed to infer an
isometric representation, disentangling the agent from the object and correctly
extracting their locations. We evaluate empirically our framework on a variety
of scenarios, showing that it outperforms vision-based approaches such as a
state-of-the-art keypoint extractor. We moreover demonstrate how the extracted
representations enable the agent to solve downstream tasks via reinforcement
learning in an efficient manner
An Efficient and Continuous Voronoi Density Estimator
We introduce a non-parametric density estimator deemed Radial Voronoi Density
Estimator (RVDE). RVDE is grounded in the geometry of Voronoi tessellations and
as such benefits from local geometric adaptiveness and broad convergence
properties. Due to its radial definition RVDE is moreover continuous and
computable in linear time with respect to the dataset size. This amends for the
main shortcomings of previously studied VDEs, which are highly discontinuous
and computationally expensive. We provide a theoretical study of the modes of
RVDE as well as an empirical investigation of its performance on
high-dimensional data. Results show that RVDE outperforms other non-parametric
density estimators, including recently introduced VDEs.Comment: 12 page
Enabling Visual Action Planning for Object Manipulation through Latent Space Roadmap
We present a framework for visual action planning of complex manipulation
tasks with high-dimensional state spaces, focusing on manipulation of
deformable objects. We propose a Latent Space Roadmap (LSR) for task planning,
a graph-based structure capturing globally the system dynamics in a
low-dimensional latent space. Our framework consists of three parts: (1) a
Mapping Module (MM) that maps observations, given in the form of images, into a
structured latent space extracting the respective states, that generates
observations from the latent states, (2) the LSR which builds and connects
clusters containing similar states in order to find the latent plans between
start and goal states extracted by MM, and (3) the Action Proposal Module that
complements the latent plan found by the LSR with the corresponding actions. We
present a thorough investigation of our framework on two simulated box stacking
tasks and a folding task executed on a real robot
Latent Space Roadmap for Visual Action Planning of Deformable and Rigid Object Manipulation
We present a framework for visual action planning of complex manipulation
tasks with high-dimensional state spaces such as manipulation of deformable
objects. Planning is performed in a low-dimensional latent state space that
embeds images. We define and implement a Latent Space Roadmap (LSR) which is a
graph-based structure that globally captures the latent system dynamics. Our
framework consists of two main components: a Visual Foresight Module (VFM) that
generates a visual plan as a sequence of images, and an Action Proposal Network
(APN) that predicts the actions between them. We show the effectiveness of the
method on a simulated box stacking task as well as a T-shirt folding task
performed with a real robot.Comment: Project website: https://visual-action-planning.github.io/lsr
Benchmarking bimanual cloth manipulation
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Cloth manipulation is a challenging task that, despite its importance, has received relatively little attention compared to rigid object manipulation. In this paper, we provide three benchmarks for evaluation and comparison of different approaches towards three basic tasks in cloth manipulation: spreading a tablecloth over a table, folding a towel, and dressing. The tasks can be executed on any bimanual robotic platform and the objects involved in the tasks are standardized and easy to acquire. We provide several complexity levels for each task, and describe the quality measures to evaluate task execution. Furthermore, we provide baseline solutions for all the tasks and evaluate them according to the proposed metrics.Peer ReviewedPostprint (author's final draft