Learning from previously collected datasets of expert data offers the pr...
We present a data-efficient framework for solving sequential decision-ma...
Reinforcement learning methods can achieve significant performance but
r...
Few-shot adaptation is a challenging problem in the context of
simulatio...
We present a data-efficient framework for solving deep visuomotor sequen...
We present a reinforcement learning based framework for human-centered
c...
To coordinate actions with an interaction partner requires a constant
ex...
Deep reinforcement learning (RL) has enabled training action-selection
p...
Modern reinforcement learning methods suffer from low sample efficiency ...
Training end-to-end deep robot policies requires a lot of domain-, task-...
In collaborative tasks, people rely both on verbal and non-verbal cues
s...
Multi-objective reinforcement learning (MORL) is the generalization of
s...
Mobile robot navigation in complex and dynamic environments is a challen...