2 citations · 5 across the 5 of their papers we have counts for
5 papers
Deep Generative Models in Robotics: A Survey on Learning from Multimodal Demonstrations
Julen Urain, Ajay Mandlekar, Yilun Du +5
Learning from Demonstrations, the field that proposes to learn robot behavior models from data, is gaining popularity with the emergence of deep generative models. Although the pro…
Transition State Clustering for Interaction Segmentation and Learning
Fabian Hahne, Vignesh Prasad, Alap Kshirsagar +4
Hidden Markov Models with an underlying Mixture of Gaussian structure have proven effective in learning Human-Robot Interactions from demonstrations for various interactive tasks v…
Kinematically Constrained Human-like Bimanual Robot-to-Human Handovers
Yasemin Göksu, Antonio De Almeida Correia, Vignesh Prasad +4
Bimanual handovers are crucial for transferring large, deformable or delicate objects. This paper proposes a framework for generating kinematically constrained human-like bimanual…
Robust Adversarial Reinforcement Learning via Bounded Rationality Curricula
Aryaman Reddi, Maximilian Tölle, Jan Peters +2
Robustness against adversarial attacks and distribution shifts is a long-standing goal of Reinforcement Learning (RL). To this end, Robust Adversarial Reinforcement Learning (RARL)…
Accelerating Motion Planning via Optimal Transport
An T. Le, Georgia Chalvatzaki, Armin Biess +1
Motion planning is still an open problem for many disciplines, e.g., robotics, autonomous driving, due to their need for high computational resources that hinder real-time, efficie…