Learning Dense Reward with Temporal Variant Self-Supervision

Yuning Wu; Jieliang Luo; Hui Li

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arXiv · 2205.10431

Learning Dense Reward with Temporal Variant Self-Supervision

Yuning Wu·Jieliang Luo·Hui Li

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Abstract

Rewards play an essential role in reinforcement learning. In contrast to rule-based game environments with well-defined reward functions, complex real-world robotic applications, such as contact-rich manipulation, lack explicit and informative descriptions that can directly be used as a reward. Previous effort has shown that it is possible to algorithmically extract dense rewards directly from multimodal observations. In this paper, we aim to extend this effort by proposing a more efficient and robust way of sampling and learning. In particular, our sampling approach utilizes temporal variance to simulate the fluctuating state and action distribution of a manipulation task. We then proposed a network architecture for self-supervised learning to better incorporate temporal information in latent representations. We tested our approach in two experimental setups, namely joint-assembly and door-opening. Preliminary results show that our approach is effective and efficient in learning dense rewards, and the learned rewards lead to faster convergence than baselines.

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BibTeX RIS

Yuning Wu, Jieliang Luo, Hui Li. 2022-05-26. Learning Dense Reward with Temporal Variant Self-Supervision. https://arxiv.org/abs/2205.10431

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