103 citations · 192 across the 31 of their papers we have counts for
7 papers · 1 filter
Clone Swarms: Learning to Predict and Control Multi-Robot Systems by Imitation
Siyu Zhou, Mariano Phielipp, Jorge A. Sefair +2
In this paper, we propose SwarmNet -- a neural network architecture that can learn to predict and imitate the behavior of an observed swarm of agents in a centralized manner. Teste…
Imitation Learning of Robot Policies by Combining Language, Vision and Demonstration
Simon Stepputtis, Joseph Campbell, Mariano Phielipp +2
In this work we propose a novel end-to-end imitation learning approach which combines natural language, vision, and motion information to produce an abstract representation of a ta…
Improved Exploration through Latent Trajectory Optimization in Deep Deterministic Policy Gradient
Kevin Sebastian Luck, Mel Vecerik, Simon Stepputtis +2
Model-free reinforcement learning algorithms such as Deep Deterministic Policy Gradient (DDPG) often require additional exploration strategies, especially if the actor is of determ…
Data-efficient Co-Adaptation of Morphology and Behaviour with Deep Reinforcement Learning
Kevin Sebastian Luck, Heni Ben Amor, Roberto Calandra
Humans and animals are capable of quickly learning new behaviours to solve new tasks. Yet, we often forget that they also rely on a highly specialized morphology that co-adapted wi…
Multimodal Dataset of Human-Robot Hugging Interaction
Kunal Bagewadi, Joseph Campbell, Heni Ben Amor
A hug is a tight embrace and an expression of warmth, sympathy and camaraderie. Despite the fact that a hug often only takes a few seconds, it is filled with details and nuances an…
Learning Interactive Behaviors for Musculoskeletal Robots Using Bayesian Interaction Primitives
Joseph Campbell, Arne Hitzmann, Simon Stepputtis +3
Musculoskeletal robots that are based on pneumatic actuation have a variety of properties, such as compliance and back-drivability, that render them particularly appealing for huma…
Probabilistic Multimodal Modeling for Human-Robot Interaction Tasks
Joseph Campbell, Simon Stepputtis, Heni Ben Amor
Human-robot interaction benefits greatly from multimodal sensor inputs as they enable increased robustness and generalization accuracy. Despite this observation, few HRI methods ar…