Review for NeurIPS paper: Learning Affordance Landscapes for Interaction Exploration in 3D Environments

Weaknesses: - Some technical details deserves more elaborations, especially on the multi-task learning. The main idea of this paper is to train an RL agents simultaneously with an affordance map segmentation network, though these are essentially treated as two orthogonal objectives, the learning procedure in a whole is still unclear to me. The authors are suggested to provide more details on how the training is proceeded, such as whether these two tasks are trained concurrently or alternatively, and if their learning processes are not synchronous, how they choose the ratio of the learning iteration of each task, and how these extra parameters can affect the performances (in an extra ablation study). A comprehensive loss function and pseudo code will be preferred. To name a few: a) The selected metrics are only evaluated in a limited range.