2,904 research outputs found
Multimodal Hierarchical Dirichlet Process-based Active Perception
In this paper, we propose an active perception method for recognizing object
categories based on the multimodal hierarchical Dirichlet process (MHDP). The
MHDP enables a robot to form object categories using multimodal information,
e.g., visual, auditory, and haptic information, which can be observed by
performing actions on an object. However, performing many actions on a target
object requires a long time. In a real-time scenario, i.e., when the time is
limited, the robot has to determine the set of actions that is most effective
for recognizing a target object. We propose an MHDP-based active perception
method that uses the information gain (IG) maximization criterion and lazy
greedy algorithm. We show that the IG maximization criterion is optimal in the
sense that the criterion is equivalent to a minimization of the expected
Kullback--Leibler divergence between a final recognition state and the
recognition state after the next set of actions. However, a straightforward
calculation of IG is practically impossible. Therefore, we derive an efficient
Monte Carlo approximation method for IG by making use of a property of the
MHDP. We also show that the IG has submodular and non-decreasing properties as
a set function because of the structure of the graphical model of the MHDP.
Therefore, the IG maximization problem is reduced to a submodular maximization
problem. This means that greedy and lazy greedy algorithms are effective and
have a theoretical justification for their performance. We conducted an
experiment using an upper-torso humanoid robot and a second one using synthetic
data. The experimental results show that the method enables the robot to select
a set of actions that allow it to recognize target objects quickly and
accurately. The results support our theoretical outcomes.Comment: submitte
Streaming Scene Maps for Co-Robotic Exploration in Bandwidth Limited Environments
This paper proposes a bandwidth tunable technique for real-time probabilistic
scene modeling and mapping to enable co-robotic exploration in communication
constrained environments such as the deep sea. The parameters of the system
enable the user to characterize the scene complexity represented by the map,
which in turn determines the bandwidth requirements. The approach is
demonstrated using an underwater robot that learns an unsupervised scene model
of the environment and then uses this scene model to communicate the spatial
distribution of various high-level semantic scene constructs to a human
operator. Preliminary experiments in an artificially constructed tank
environment as well as simulated missions over a 10m10m coral reef
using real data show the tunability of the maps to different bandwidth
constraints and science interests. To our knowledge this is the first paper to
quantify how the free parameters of the unsupervised scene model impact both
the scientific utility of and bandwidth required to communicate the resulting
scene model.Comment: 8 pages, 6 figures, accepted for presentation in IEEE Int. Conf. on
Robotics and Automation, ICRA '19, Montreal, Canada, May 201
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