Publication

A Generative Model for Intention Recognition and Manipulation Assistance in Teleoperation

Publications associées (36)

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From High-Level to Low-Level Robot Learning of Complex Tasks: Leveraging Priors, Metrics and Dynamical Systems

Nadia Barbara Figueroa Fernandez

Humans have a remarkable way of learning, adapting and mastering new manipulation tasks. With the current advances in Machine Learning (ML), the promise of having robots with such capabilities seems to be on the cusp of reality. Transferring human-level sk ...

EPFL2019

Learning from demonstration for semi-autonomous teleoperation

Sylvain Calinon

Teleoperation in domains such as deep-sea or space often requires the completion of a set of recurrent tasks. We present a framework that uses a probabilistic approach to learn from demonstration models of manipulation tasks. We show how such a framework c ...

2019

Generative Models for Learning Robot Manipulation Skills from Humans

Ajay Kumar Tanwani

A long standing goal in artificial intelligence is to make robots seamlessly interact with humans in performing everyday manipulation skills. Learning from demonstrations or imitation learning provides a promising route to bridge this gap. In contrast to d ...

EPFL2018

Incorporating Human Expertise in Robot Motion Learning and Synthesis

Hang Yin

With the exponential growth of robotics and the fast development of their advanced cognitive and motor capabilities, one can start to envision humans and robots jointly working together in unstructured environments. Yet, for that to be possible, robots nee ...

EPFL2018

Variational Methods for Human Modeling

Timur Bagautdinov

A large part of computer vision research is devoted to building models and algorithms aimed at understanding human appearance and behaviour from images and videos. Ultimately, we want to build automated systems that are at least as capable as people when i ...

EPFL2018

Applications of Approximate Learning and Inference for Probabilistic Models

Young Jun Ko

We develop approximate inference and learning methods for facilitating the use of probabilistic modeling techniques motivated by applications in two different areas. First, we consider the ill-posed inverse problem of recovering an image from an underdeter ...

EPFL2017

Design considerations for implantable and wearable antennas

Anja Skrivervik

This contribution gives a review of our results characterizing the propagation in lossy biological tissues. These results were mainly obtained using a simplified spherical model allowing a description in spherical wave decomposition. This model is of cours ...

IEEE2017

Learning assistive teleoperation behaviors from demonstration

Sylvain Calinon

Emergency response in hostile environments often involves remotely operated vehicles (ROVs) that are teleoperated as interaction with the environment is typically required. Many ROV tasks are common to such scenarios and are often recurrent. We show how a ...

2016

Stochastic variational learning in recurrent spiking networks

Wulfram Gerstner

The ability to learn and perform statistical inference with biologically plausible recurrent networks of spiking neurons is an important step toward understanding perception and reasoning. Here we derive and investigate a new learning rule for recurrent sp ...

Frontiers Research Foundation2014

On the Relationship Between Environmental and Mechanical Complexity in Evolved Robots

Joshua Evan Auerbach

According to the principles of embodied cognition, intelligent behavior must arise out of the coupled dynamics of an agent's brain, body, and environment. This suggests that the morphological complexity of a robot should scale in relation to the complexity ...

MIT Press2012