We present the qualitative analysis of this method in this repo. We have uploaded the videos of lift environments and can environments with both GMM and Diffusion base policy, and after online learning with our method. We can observe that the base policy is imporved by our method in two ways - 
1. First, it makes the base policy more robust by increasing the success rate to 100% 
2. Secondly, it makes the base policy more efficient by completing the task in fewer timesteps    

Uploaded Videos - 
1. lift_env_base_policy.py -> GMM Base Policy for Lift Environment
2. lift_env_our_policy.py -> Uncertainty aware Residual RL with GMM Base Policy for Lift Environment
3. can_env_base_gmm_policy.py -> GMM Base Policy for Can Environment
4. can_env_our_gmm_policy.py -> Uncertainty aware Residual RL with GMM Base Policy for Can Environment
5. can_env_base_diffusion_policy.py -> Diffusion Base Policy for Can Environment
6. can_env_our_diffusion_policy.py -> Uncertainty aware Residual RL with Diffusion Base Policy for Can Environment
