15 citations · 15 across the 5 of their papers we have counts for
5 papers
Epistemically-guided forward-backward exploration
Núria Armengol Urpí, Marin Vlastelica, Georg Martius +1
Zero-shot reinforcement learning is necessary for extracting optimal policies in absence of concrete rewards for fast adaptation to future problem settings. Forward-backward repres…
CAIMAN: Causal Action Influence Detection for Sample-efficient Loco-manipulation
Yuanchen Yuan, Jin Cheng, Núria Armengol Urpí +1
Enabling legged robots to perform non-prehensile loco-manipulation is crucial for enhancing their versatility. Learning behaviors such as whole-body object pushing often requires s…
Causal Action Influence Aware Counterfactual Data Augmentation
Núria Armengol Urpí, Marco Bagatella, Marin Vlastelica +1
Offline data are both valuable and practical resources for teaching robots complex behaviors. Ideally, learning agents should not be constrained by the scarcity of available demons…
Efficient Learning of High Level Plans from Play
Núria Armengol Urpí, Marco Bagatella, Otmar Hilliges +2
Real-world robotic manipulation tasks remain an elusive challenge, since they involve both fine-grained environment interaction, as well as the ability to plan for long-horizon goa…
Risk-Averse Offline Reinforcement Learning
Núria Armengol Urpí, Sebastian Curi, Andreas Krause
Training Reinforcement Learning (RL) agents in high-stakes applications might be too prohibitive due to the risk associated to exploration. Thus, the agent can only use data previo…