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This research aims to address the challenge of sparse rewards in reinforcement learning for manipulator grasping tasks.

March 7, 2026TechnologiesOpen Access

PHER: A Method for Solving the Sparse Reward Problem of a Manipulator Grasping Task

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This research aims to address the challenge of sparse rewards in reinforcement learning for manipulator grasping tasks.
Utilized off-policy reinforcement learning for the grasping task model.
Employed hindsight experience replay (HER) to relabel completed states.
Introduced a prioritized sampling method for selecting relabeled transitions from the experience replay buffer.
Combined prioritized sampling with various off-policy reinforcement learning algorithms for training.
PHER demonstrated significantly faster convergence than standard HER methods.
Improved data utilization was observed due to the prioritized sampling approach.

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Off-policy reinforcement learning improves training efficiency in manipulation tasks, suggesting better data usage.

Zhang et al. (2026) studied this question.

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