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Uncertainty-driven Exploration Strategies for Online Grasp Learning
Existing grasp prediction approaches are mostly based on offline learning, while, ignored the exploratory grasp learning during online adaptation to new picking scenarios, i. e., unseen object portfolio, camera and bin settings etc.
A Hybrid Approach for Learning to Shift and Grasp with Elaborate Motion Primitives
Many possible fields of application of robots in real world settings hinge on the ability of robots to grasp objects.
SOLO: Search Online, Learn Offline for Combinatorial Optimization Problems
This method can equally be applied to both the offline, as well as online, variants of the combinatorial problem, in which the problem components (e. g., jobs in scheduling problems) are not known in advance, but rather arrive during the decision-making process.
Monte-Carlo Planning: Theoretically Fast Convergence Meets Practical Efficiency
Popular Monte-Carlo tree search (MCTS) algorithms for online planning, such as epsilon-greedy tree search and UCT, aim at rapidly identifying a reasonably good action, but provide rather poor worst-case guarantees on performance improvement over time.
