Humanoid home robots are on the market - but do we really want them? Last year, Norwegian-US tech company 1X announced a strange new product: "the world's first consumer-ready humanoid robot designed to transform life at home". Standing 168 centimetres tall and weighing in at 30 kilograms, the US$20,000 Neo bot promises to automate common household chores such as folding laundry and loading the dishwasher. Neo has a built-in artificial intelligence (AI) system, but for tricky tasks it requires a 1X employee wearing a virtual reality helmet to remotely take over the robot. The operator can see whatever the bot does inside your house, and the process is recorded for future learning.

In order to help clarify our contributions and or-2 ganize them for readers, we provide the following table to summarize the differences between regrets.3 REVIEWER 4 Thank you for your comments. Concept drift occurs when the optimal model attimetmay no longer bethe optimal model10 at timet+1. Consider an online learning problem with concept drift withT = 3 time periods and loss functions:11 f1(x) = (x 1)2,f2(x) = (x 2)2,f3(x) = (x 3)2. Figure 1: SGD online with momentum Theoretical motivation via Calibration: A more formal motivation of our regret23 can be related to the concept of calibration [1]. The comment on line 110 can be24 rewritten as: If the updates{x1,,xT} are well-calibrated, then perturbingxt by25 anyucannot substantially reduce the cumulative loss.Hence, itcan besaid that the26 sequence {x1,,xT} is asymptotically calibrated with respect to{f1,,fT} if:27 Weindeedranexperiments usingSGDwithmomentum forvariousdecayparameters andconcluded thatSGDwith36 momentum is not even as stable as SGD-online (standard SGD without momentum) as shown in Figure 1.

This paper introduces a novel approach using Large Language Models (LLMs) integrated into an agent framework for flexible and effective personal mobility generation. LLMs overcome the limitations of previous models by effectively processing semantic data and offering versatility in modeling various tasks.

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R2: "Section 4.2 needs more clarity. It's not clear if the Fog noise model is a good model for fog corruption..."