54 citations · 74 across the 14 of their papers we have counts for
16 papers
Tool-Policy Co-Design for Powder Weighing in Laboratory Automation
Nikola Radulov, Xin Yang, Kevin S. Luck +1
Autonomous powder weighing is one of many bottlenecks in laboratory automation due to the complex, non-linear dynamics of heterogeneous materials. Robot chemists performing this ta…
Reinforcement Learning from Cross-domain Videos with Video Prediction Model
Zhao Yang, Xinrui Zu, Jacob E. Kooi +5
Reinforcement learning from expert videos across visually distinct domains is challenging due to the absence of reward signals and the presence of domain gaps. We introduce XIPER (…
Temporal Consistency Improves Generalization in Contextual Offline Meta Reinforcement Learning
Mohammadreza Nakheai, Aidan Scannell, Kevin Luck +1
Offline meta-reinforcement learning seeks to learn a policy that generalizes to new related tasks online. Context-based methods infer a task representation from transition historie…
MoDeSuite: Robot Learning Task Suite for Benchmarking Mobile Manipulation with Deformable Objects
Yuying Zhang, Kevin Sebastian Luck, Francesco Verdoja +2
Mobile manipulation is a critical capability for robots operating in diverse, real-world environments. However, manipulating deformable objects and materials remains a major challe…
Discrete Codebook World Models for Continuous Control
Aidan Scannell, Mohammadreza Nakhaei, Kalle Kujanpää +4
In reinforcement learning (RL), world models serve as internal simulators, enabling agents to predict environment dynamics and future outcomes in order to make informed decisions.…
Learning Transparent Reward Models via Unsupervised Feature Selection
Daulet Baimukashev, Gokhan Alcan, Kevin Sebastian Luck +1
In complex real-world tasks such as robotic manipulation and autonomous driving, collecting expert demonstrations is often more straightforward than specifying precise learning obj…