67,279 research outputs found
Deep Affordance-grounded Sensorimotor Object Recognition
It is well-established by cognitive neuroscience that human perception of
objects constitutes a complex process, where object appearance information is
combined with evidence about the so-called object "affordances", namely the
types of actions that humans typically perform when interacting with them. This
fact has recently motivated the "sensorimotor" approach to the challenging task
of automatic object recognition, where both information sources are fused to
improve robustness. In this work, the aforementioned paradigm is adopted,
surpassing current limitations of sensorimotor object recognition research.
Specifically, the deep learning paradigm is introduced to the problem for the
first time, developing a number of novel neuro-biologically and
neuro-physiologically inspired architectures that utilize state-of-the-art
neural networks for fusing the available information sources in multiple ways.
The proposed methods are evaluated using a large RGB-D corpus, which is
specifically collected for the task of sensorimotor object recognition and is
made publicly available. Experimental results demonstrate the utility of
affordance information to object recognition, achieving an up to 29% relative
error reduction by its inclusion.Comment: 9 pages, 7 figures, dataset link included, accepted to CVPR 201
Real-time Monocular Object SLAM
We present a real-time object-based SLAM system that leverages the largest
object database to date. Our approach comprises two main components: 1) a
monocular SLAM algorithm that exploits object rigidity constraints to improve
the map and find its real scale, and 2) a novel object recognition algorithm
based on bags of binary words, which provides live detections with a database
of 500 3D objects. The two components work together and benefit each other: the
SLAM algorithm accumulates information from the observations of the objects,
anchors object features to especial map landmarks and sets constrains on the
optimization. At the same time, objects partially or fully located within the
map are used as a prior to guide the recognition algorithm, achieving higher
recall. We evaluate our proposal on five real environments showing improvements
on the accuracy of the map and efficiency with respect to other
state-of-the-art techniques
Belief Tree Search for Active Object Recognition
Active Object Recognition (AOR) has been approached as an unsupervised
learning problem, in which optimal trajectories for object inspection are not
known and are to be discovered by reducing label uncertainty measures or
training with reinforcement learning. Such approaches have no guarantees of the
quality of their solution. In this paper, we treat AOR as a Partially
Observable Markov Decision Process (POMDP) and find near-optimal policies on
training data using Belief Tree Search (BTS) on the corresponding belief Markov
Decision Process (MDP). AOR then reduces to the problem of knowledge transfer
from near-optimal policies on training set to the test set. We train a Long
Short Term Memory (LSTM) network to predict the best next action on the
training set rollouts. We sho that the proposed AOR method generalizes well to
novel views of familiar objects and also to novel objects. We compare this
supervised scheme against guided policy search, and find that the LSTM network
reaches higher recognition accuracy compared to the guided policy method. We
further look into optimizing the observation function to increase the total
collected reward of optimal policy. In AOR, the observation function is known
only approximately. We propose a gradient-based method update to this
approximate observation function to increase the total reward of any policy. We
show that by optimizing the observation function and retraining the supervised
LSTM network, the AOR performance on the test set improves significantly.Comment: IROS 201
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