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cs.RO2026
Disentangling Task-Relevant Latent Dynamics for Reward Modeling
Dayou Li, Jionghao Wang, Peihao Wang +9
Reward design remains a major bottleneck for reinforcement learning in robotic manipulation. Handcrafted dense rewards can compensate for the limited guidance provided by sparse re…
cs.RO2026
Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration
Youwei Yu, Jionghao Wang, Zhengming Yu +2
Designing learnable information-theoretic objectives for robot exploration remains challenging. Such objectives aim to guide exploration toward data that reduces uncertainty in mod…