Hello, thank you for open sourcing your work and weights along with the extensive documentation. I am working through real-world implementation and noticed in internvla_n1_agent_realworld.py the following code is used to generate image context around line 190 shown in the first code preview below. This function makes the history always begin at 0 and give num_history spaced indices up to episode_idx-1 resulting in history id lists such as [0, 4,9, 13, 18, 22, 27, 32]. In Figure 2 of your paper, it seems like the context should be the previous num_history images. Could you explain whether we want to give this kind of context or the history id should be defined like the second code option?
history_id = np.unique(np.linspace(0, self.episode_idx - 1, self.num_history, dtype=np.int32)).tolist()
min_indx = max(0, self.episode_idx - self.num_history)
max_indx = self.episode_idx
history_id = [i for i in range(min_indx, max_indx)]
Hello, thank you for open sourcing your work and weights along with the extensive documentation. I am working through real-world implementation and noticed in internvla_n1_agent_realworld.py the following code is used to generate image context around line 190 shown in the first code preview below. This function makes the history always begin at 0 and give num_history spaced indices up to episode_idx-1 resulting in history id lists such as [0, 4,9, 13, 18, 22, 27, 32]. In Figure 2 of your paper, it seems like the context should be the previous num_history images. Could you explain whether we want to give this kind of context or the history id should be defined like the second code option?
history_id = np.unique(np.linspace(0, self.episode_idx - 1, self.num_history, dtype=np.int32)).tolist()