Publications by Aude Billard | EPFL Graph Search

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This abstract addresses the question of human imitation through convergent evidence from neuroscience. We look at deficits in imitation following brain lesion, such as apraxia. We believe that looking at how imitation is impaired can unveil its underlying principles. We also take inspiration from numerous brain imaging studies to ground the functional architecture and information flow of our model. In the end we will use findings from monkey brain neurophysiological studies to implement the details of our p

Schizophrenia patients are known to be impaired in their ability to process social information and to engage in social interactions. To understand better social cognition in schizophrenia, we investigate the links between these impairments. In this paper, we focus primarily on the influence of social feedback, such as facial emotions, on motor coordination during joint action. To investigate and quantify this influence, we exploited systematically-controlled social and nonsocial feedback provided by a human

Robot learning by imitation makes an increasing body of robotics research. Imitation learning complements motor learning techniques by restricting the search space to a computationally tractable subset. Imitation learning search for spatial and temporal invariants across several demonstrations. These invariants are task- dependent. This talk will present an algorithm that determines the key features of an imitation task through a comparative analysis of the data in joint space, carthesian space and visual s

Decision making and planning for which the state information is only partially available is a problem faced by all forms of intelligent entities they being either virtual, synthetic or biological. The standard approach to mathematically solve such a decisi ...

Efficient and safe multi-agent swarm coordination in environments where humans operate, such as warehouses, assistive living rooms, or automated hospitals, is crucial for adopting automation. In this paper, we augment the obstacle avoidance algorithm based ...

We present a Programming by Demonstration (PbD) framework for generically extracting the relevant features of a given task and for addressing the problem of generalizing the acquired knowledge to different contexts. We validate the architecture through a s ...

In this paper, we consider a novel approach to control the timing of motions when these are encoded with autonomous dynamical systems (DS). Accurate timing of motion is crucial if a robot must synchronize its movement with that of a fast moving object. In ...

This paper presents a method by which a robot can learn through observation to perform a collaborative manipulation task, namely lifting an object. The task is first demonstrated by a user controlling the robot's hand via a haptic interface. Learning extra ...

Object manipulation is a challenging task for robotics, as the physics involved in object interaction is com- plex and hard to express analytically. Here we introduce a modular approach for learning a manipulation strategy from human demonstration. Firstly ...

To fulfill a need for natural, user-friendly means of interacting and reprogramming toy and humanoid robots, a growing trend of robotics research investigates the integration of methods for gesture recognition and natural speech processing. Unfortunately, efficient methods for speech and vision processing remain computationally expensive and, thus, cannot be easily exploited on cost- and size-limited platforms. Personal Digital Assistants (PDAs) are ideal low-cost platforms to provide simple speech and visi

A critical need in assistive robotics, such as assistive wheelchairs for navigation, is a need to learn task intent and safety guarantees through user interactions in order to ensure safe task performance. For tasks where the objectives from the user are n ...

Imagine stepping away from your desk and reaching out for your office's light switch with your eyes closed. You might manage to complete the task successfully but might instead hit the corner of your desk or knock over the coffee mug your colleague just le ...

Gaussian process regression is widely used because of its ability to provide well-calibrated uncertainty estimates and handle small or sparse datasets. However, it struggles with high-dimensional data. One possible way to scale this technique to higher dimensions is to leverage the implicit low-dimensional manifold upon which the data actually lies, as postulated by the manifold hypothesis. Prior work ordinarily requires the manifold structure to be explicitly provided though, i.e. given by a mesh or be kno

This paper surveys some of the main ethical issues pertaining to robotics that have been discussed in the literature. We start with the idea of responsibility ascription that arises when an autonomous system malfunctions or harms people. Next, we discuss v ...

Existing research that addressed cable manipulation relied on two-fingered grippers, which make it difficult to perform similar cable manipulation tasks that humans perform. However, unlike dexterous manipulation of rigid objects, the development of dexterous cable manipulation skills in robotics remains underexplored due to the unique challenges posed by a cable's deformability and inherent uncertainty. In addition, using a dexterous hand introduces specific difficulties in tasks, such as cable grasping, p

Dynamical System (DS)-based closed-loop control is a simple and effective way to generate reactive motion policies that well generalize to the robotic workspace, while retaining stability guarantees. Lately the formalism has been expanded in order to handl ...

Robustness to perturbation has been advocated as a key element to robot control and efforts in that direction are numerous. While in essence these approaches aim at "endowing robots with a flexibility similar to that displayed by humans", few have actually ...

The inverse dynamics of a robotic manipulator is instrumental in precise robot control and manipulation. However, acquiring such a model is challenging, not only due to unmodelled non-linearities such as joint friction, but also from a machine learning per ...

Dexterous in-hand manipulation in robotics, particularly with multi-fingered robotic hands, poses significant challenges due to the intricate avoidance of collisions among fingers and the object being manipulated. Collision-free paths for all fingers must ...

Many daily tasks involve the collaboration of both hands. Humans dexterously adjust hand poses and modulate the forces exerted by fingers in response to task demands. Hand pose selection has been intensively studied in unimanual tasks, but little work has ...

Aude Billard

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Neural Modeling of Imitation Deficits

Effects of Facial Emotions on Social-motor Coordination in Schizophrenia

Computational elements of robot learning by imitation

Learning search polices from humans in a partially observable context

Agent Prioritization and Virtual Drag Minimization in Dynamical System Modulation For Obstacle Avoidance of Decentralized Swarms

On Learning, Representing and Generalizing a Task in a Humanoid Robot

Learning Motion Dynamics to Catch a Moving Object

Learning collaborative manipulation tasks by demonstration using a haptic interface

A Modular Approach to Learning Manipulation Strategies from Human Demonstration

PDA Interface for Humanoid Robots

Credit Assignment Safety Learning from Human Demonstrations

Robots that share our spaces require proprioception and a sense of touch

Implicit Manifold Gaussian Process Regression

The Ethical Landscape of Robotics

Dexterous Cable Manipulation: Taxonomy, Multi-Fingered Hand Design, and Long-Horizon Manipulation

Hybrid Quadratic Programming - Pullback Bundle Dynamical Systems Control

Coupled dynamical system based hand-arm grasp planning under real-time perturbations

Efficient Configuration Exploration in Inverse Dynamics Acquisition of Robotic Manipulators

Enhancing Dexterity in Confined Spaces: Real-Time Motion Planning for Multifingered In-Hand Manipulation

Hand pose selection in a bimanual fine-manipulation task