2 citations · 4 across the 3 of their papers we have counts for
5 papers · 1 filter
Modularity through Attention: Efficient Training and Transfer of Language-Conditioned Policies for Robot Manipulation
Yifan Zhou, Shubham Sonawani, Mariano Phielipp +2
Language-conditioned policies allow robots to interpret and execute human instructions. Learning such policies requires a substantial investment with regards to time and compute re…
Language-Conditioned Imitation Learning for Robot Manipulation Tasks
Simon Stepputtis, Joseph Campbell, Mariano Phielipp +3
Imitation learning is a popular approach for teaching motor skills to robots. However, most approaches focus on extracting policy parameters from execution traces alone (i.e., moti…
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…
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…