Zeyu Gao

Pretrained generative policies give robot control strong behavior priors, but adapting them efficiently with online reinforcement learning remains hard: noise-space residual methods regulate adaptation through fixed latent feasible regions that may exclude valuable noise edits, while action-space residual methods are more sample-efficient yet still leave room for improvement. CNDO drops predefined latent feasible regions, learning state- and noise-conditioned directions regulated by a soft conservative constraint built from conservative value estimation and gated execution; CANDO adds an action-space residual on top. Experiments report 76.1 NAUC and a 91.4% final success rate for CANDO, 6.6 NAUC and 4.1 percentage points above the best single-space results.

Pretrained generative policies give robot control strong behavior priors, but adapting them efficiently with online reinforcement learning remains hard: noise-space residual methods regulate adaptation through fixed latent feasible regions that may exclude valuable noise edits, while action-space residual methods are more sample-efficient yet still leave room for improvement. CNDO drops predefined latent feasible regions, learning state- and noise-conditioned directions regulated by a soft conservative constraint built from conservative value estimation and gated execution; CANDO adds an action-space residual on top. Experiments report 76.1 NAUC and a 91.4% final success rate for CANDO, 6.6 NAUC and 4.1 percentage points above the best single-space results.