Research
CANDO: Conservative Action-Noise Directional Optimization for Residual Reinforcement Learning
This paper introduces CANDO, a residual reinforcement learning method that improves frozen generative policies by combining noise-space and action-space residual optimization. CANDO removes fixed latent feasible regions and uses conservative value estimation with gated execution to regulate adaptation. Experiments show CANDO achieves 76.1 NAUC and 91.4% final success rate, surpassing single-space baselines by 6.6 NAUC and 4.1%.
Reinforcement LearningRobot ControlPolicy Adaptation