12 citations · 13 across the 6 of their papers we have counts for
9 papers · 1 filter
Learning Human Reaching Optimality Principles from Minimal Observation Inverse Reinforcement Learning
Sarmad Mehrdad, Maxime Sabbah, Vincent Bonnet +1
This paper investigates the application of Minimal Observation Inverse Reinforcement Learning (MO-IRL) to model and predict human arm-reaching movements with time-varying cost weig…
Safe and Performant Deployment of Autonomous Systems via Model Predictive Control and Hamilton-Jacobi Reachability Analysis
Hao Wang, Armand Jordana, Ludovic Righetti +1
While we have made significant algorithmic developments to enable autonomous systems to perform sophisticated tasks, it remains difficult for them to perform tasks effective and sa…
Safe Reinforcement Learning of Robot Trajectories in the Presence of Moving Obstacles
Jonas Kiemel, Ludovic Righetti, Torsten Kröger +1
In this paper, we present an approach for learning collision-free robot trajectories in the presence of moving obstacles. As a first step, we train a backup policy to generate evas…
iDb-RRT: Sampling-based Kinodynamic Motion Planning with Motion Primitives and Trajectory Optimization
Joaquim Ortiz-Haro, Wolfgang Hönig, Valentin N. Hartmann +2
Rapidly-exploring Random Trees (RRT) and its variations have emerged as a robust and efficient tool for finding collision-free paths in robotic systems. However, adding dynamic con…
Risk-Sensitive Extended Kalman Filter
Armand Jordana, Avadesh Meduri, Etienne Arlaud +2
In robotics, designing robust algorithms in the face of estimation uncertainty is a challenging task. Indeed, controllers often do not consider the estimation uncertainty and only…
Path Planning Under Uncertainty to Localize mmWave Sources
Kai Pfeiffer, Yuze Jia, Mingsheng Yin +8
In this paper, we study a navigation problem where a mobile robot needs to locate a mmWave wireless signal. Using the directionality properties of the signal, we propose an estimat…