5 citations · 9 across the 7 of their papers we have counts for
8 papers · 1 filter
From Obstacle Avoidance To Motion Learning Using Local Rotation of Dynamical Systems
Lukas Huber, Jean-Jacques Slotine, Aude Billard
In robotics motion is often described from an external perspective, i.e., we give information on the obstacle motion in a mathematical manner with respect to a specific (often iner…
User-specific, Adaptable Safety Controllers Facilitate User Adoption in Human-Robot Collaboration
Ahalya Prabhakar, Aude Billard
As assistive and collaborative robots become more ubiquitous in the real-world, we need to develop interfaces and controllers that are safe for users to build trust and encourage a…
Fast Obstacle Avoidance Based on Real-Time Sensing
Lukas Huber, Aude Billard, Jean-Jacques Slotine
Humans are remarkable at navigating and moving through dynamic and complex spaces, such as crowded streets. For robots to do the same, it is crucial that they are endowed with high…
Multimodal Sensory Learning for Real-time, Adaptive Manipulation
Ahalya Prabhakar, Stanislas Furrer, Lorenzo Panchetti +2
Adaptive control for real-time manipulation requires quick estimation and prediction of object properties. While robot learning in this area primarily focuses on using vision, many…
Credit Assignment Safety Learning from Human Demonstrations
Ahalya Prabhakar, Aude Billard
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 ensu…
Crowd against the machine: A simulation-based benchmark tool to evaluate and compare robot capabilities to navigate a human crowd
Fabien Grzeskowiak, David Gonon, Daniel Dugas +7
The evaluation of robot capabilities to navigate human crowds is essential to conceive new robots intended to operate in public spaces. This paper initiates the development of a be…