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Found 5 papers, 0 papers with code
PARTNR: Pick and place Ambiguity Resolving by Trustworthy iNteractive leaRning
We extend previous works and present the PARTNR algorithm that can detect ambiguities in the trained policy by analyzing multiple modalities in the pick and place poses using topological analysis.
Overview of Tools Supporting Planning for Automated Driving
Planning is an essential topic in the realm of automated driving.
A novel approach to model exploration for value function learning
Planning and Learning are complementary approaches.
Safe learning-based optimal motion planning for automated driving
This paper presents preliminary work on learning the search heuristic for the optimal motion planning for automated driving in urban traffic.
A novel model-based heuristic for energy optimal motion planning for automated driving
Although planning of an optimal trajectory is done in a systematic way, dynamic programming does not use any knowledge about the considered problem to guide the exploration and therefore explores all possible trajectories.
