Deep Learning Approaches for Auditory Perception in Robotics
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This work considers our approach to robot motion control learning from the standpoint of multiple data sources. Our paradigm derives data from human teachers providing task demonstrations and tactile corrections for policy refinement and reuse. We contribu ...
This study attempts to make a compact humanoid robot acquire a giant-swing motion without any robotic models by using reinforcement learning; only the interaction with environment is available. Generally, it is widely said that this type of learning method ...
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Compared to daily rainfall data, observed subdaily rainfall times are rare and often very short. For hydrologic modeling, this problem is often addressed by generating synthetic hourly rainfall series, with rainfall generators calibrated on relevant rainfa ...
Reinforcement learning in neural networks requires a mechanism for exploring new network states in response to a single, nonspecific reward signal. Existing models have introduced synaptic or neuronal noise to drive this exploration. However, those types o ...
Structural Health Monitoring (SMH) has the potential to provide reliable, quantitative data on the real condition of structures. Early detection of damage or deterioration can enable preventive repair and reduce maintenance costs. Although obtaining data f ...
The tracking precision required by modern industrial applications is continuously increasing. Feedback control alone is often no longer capable of giving the necessary tracking accuracy and so the use of two-degree-of-freedom controllers, which include a f ...
The work of this thesis centers around the research subject of distributed robotic search. Within the field of distributed robotic systems, a task of particular interest is attempting to locate one or more targets in a possibly unknown environment. While n ...
This paper illustrates the practical application of non-iterative correlation- based tuning with guaranteed stability. In this method, a sufficient condition for closed-loop stability is defined as the H infinity-norm of a particular error function. This n ...
This paper investigates the use of a hierarchy of Neural Networks for performing data driven feature extraction. Two different hierarchical structures based on long and short temporal context are considered. Features are tested on two different LVCSR syste ...
Artificial neural networks are applied to many real-world problems, ranging from pattern classification to robot control. In order to design a neural network for a particular task, the choice of an architecture (including the choice of a neuron model), and ...