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cs.RO2023
CLIP-Motion: Learning Reward Functions for Robotic Actions Using Consecutive Observations
Xuzhe Dang, Stefan Edelkamp
This paper presents a novel method for learning reward functions for robotic motions by harnessing the power of a CLIP-based model. Traditional reward function design often hinges…
cs.AI2023★ 1 cited
Optimize Planning Heuristics to Rank, not to Estimate Cost-to-Goal
Leah Chrestien, Tomás Pevný, Stefan Edelkamp +1
In imitation learning for planning, parameters of heuristic functions are optimized against a set of solved problem instances. This work revisits the necessary and sufficient condi…