Jiafei Lyu

This framework surveys manipulation, navigation, locomotion, autonomous driving, and general embodied learning, tracing technical progressions, clarifying capability requirements, and examining datasets, benchmarks, and evaluation protocols.

Merging existing expert capabilities can yield significant performance boosts, but choosing the right fusion method can make all the difference in multi-domain reinforcement learning.

RAMBO's instability got you down? ROMI offers a robust, value-aware model learning approach with implicitly differentiable adaptive weighting that outperforms RAMBO and other SOTA methods in offline RL benchmarks.

Constraining initial state representations with a simple Tanh activation and skip connections can significantly boost off-policy RL performance, rivaling more complex methods on continuous control tasks.