3 citations · 6 across the 8 of their papers we have counts for
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cs.RO2024★ 2 cited
Equivariant Reinforcement Learning under Partial Observability
Hai Nguyen, Andrea Baisero, David Klee +3
Incorporating inductive biases is a promising approach for tackling challenging robot learning domains with sample-efficient solutions. This paper identifies partially observable d…
cs.LG2024
Equivariant Offline Reinforcement Learning
Arsh Tangri, Ondrej Biza, Dian Wang +3
Sample efficiency is critical when applying learning-based methods to robotic manipulation due to the high cost of collecting expert demonstrations and the challenges of on-robot p…