5 papers · 1 filter
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…
What Matters for Simulation to Online Reinforcement Learning on Real Robots
Yarden As, Dhruva Tirumala, René Zurbrügg +4
We investigate what specific design choices enable successful online reinforcement learning (RL) on physical robots. Across 100 real-world training runs on three distinct robotic p…
Learning Soft Robotic Dynamics with Active Exploration
Hehui Zheng, Bhavya Sukhija, Chenhao Li +3
Soft robots offer unmatched adaptability and safety in unstructured environments, yet their compliant, high-dimensional, and nonlinear dynamics make modeling for control notoriousl…
Uncertainty-Aware Robotic World Model Makes Offline Model-Based Reinforcement Learning Work on Real Robots
Chenhao Li, Andreas Krause, Marco Hutter
Reinforcement Learning (RL) has achieved impressive results in robotics, yet high-performing pipelines remain highly task-specific, with little reuse of prior data. Offline Model-b…
Robotic World Model: A Neural Network Simulator for Robust Policy Optimization in Robotics
Chenhao Li, Andreas Krause, Marco Hutter
Learning robust and generalizable world models is crucial for enabling efficient and scalable robotic control in real-world environments. In this work, we introduce a novel framewo…