103,192 research outputs found
Learning to Represent Haptic Feedback for Partially-Observable Tasks
The sense of touch, being the earliest sensory system to develop in a human
body [1], plays a critical part of our daily interaction with the environment.
In order to successfully complete a task, many manipulation interactions
require incorporating haptic feedback. However, manually designing a feedback
mechanism can be extremely challenging. In this work, we consider manipulation
tasks that need to incorporate tactile sensor feedback in order to modify a
provided nominal plan. To incorporate partial observation, we present a new
framework that models the task as a partially observable Markov decision
process (POMDP) and learns an appropriate representation of haptic feedback
which can serve as the state for a POMDP model. The model, that is parametrized
by deep recurrent neural networks, utilizes variational Bayes methods to
optimize the approximate posterior. Finally, we build on deep Q-learning to be
able to select the optimal action in each state without access to a simulator.
We test our model on a PR2 robot for multiple tasks of turning a knob until it
clicks.Comment: IEEE International Conference on Robotics and Automation (ICRA), 201
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
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