10 citations · 12 across the 6 of their papers we have counts for
6 papers
Uncertainty Quantification for Flow-Based Generalist Robot Policies
Ralf Römer, Maximilian Seeliger, Saida Liu +5
Generalist robot policies, such as vision-language-action models (VLAs) and world-action models (WAMs), combine powerful pretrained backbones with expressive generative action head…
Reinforcement Learning via Self-Distillation
Jonas Hübotter, Frederike Lübeck, Lejs Behric +8
Large language models are increasingly post-trained with reinforcement learning in verifiable domains such as code and math. Yet, current methods for reinforcement learning with ve…
FLoRA: Sample-Efficient Preference-based RL via Low-Rank Style Adaptation of Reward Functions
Daniel Marta, Simon Holk, Miguel Vasco +6
Preference-based reinforcement learning (PbRL) is a suitable approach for style adaptation of pre-trained robotic behavior: adapting the robot's policy to follow human user prefere…
The Impact of VR and 2D Interfaces on Human Feedback in Preference-Based Robot Learning
Jorge de Heuvel, Daniel Marta, Simon Holk +2
Aligning robot navigation with human preferences is essential for ensuring comfortable, and predictable robot movement in shared spaces. While preference-based learning methods, su…
PREDILECT: Preferences Delineated with Zero-Shot Language-based Reasoning in Reinforcement Learning
Simon Holk, Daniel Marta, Iolanda Leite
Preference-based reinforcement learning (RL) has emerged as a new field in robot learning, where humans play a pivotal role in shaping robot behavior by expressing preferences on d…
Human-Centric Autonomous Systems With LLMs for User Command Reasoning
Yi Yang, Qingwen Zhang, Ci Li +3
The evolution of autonomous driving has made remarkable advancements in recent years, evolving into a tangible reality. However, a human-centric large-scale adoption hinges on meet…