Recent work in sim2real has successfully enabled robots to act in physical
environments by training in simulation with a diverse ''population'' of
environments (i.e. domain randomization). In this work, we focus on enabling
generalization in assistive tasks: tasks in which the robot is acting to assist
a user (e.g. helping someone with motor impairments with bathing or with
scratching an itch). Such tasks are particularly interesting relative to prior
sim2real successes because the environment now contains a human who is also
acting. This complicates the problem because the diversity of human users
(instead of merely physical environment parameters) is more difficult to
capture in a population, thus increasing the likelihood of encountering
out-of-distribution (OOD) human policies at test time. We advocate that
generalization to such OOD policies benefits from (1) learning a good latent
representation for human policies that test-time humans can accurately be
mapped to, and (2) making that representation adaptable with test-time
interaction data, instead of relying on it to perfectly capture the space of
human policies based on the simulated population only. We study how to best
learn such a representation by evaluating on purposefully constructed OOD test
policies. We find that sim2real methods that encode environment (or population)
parameters and work well in tasks that robots do in isolation, do not work well
in assistance. In assistance, it seems crucial to train the representation
based on the history of interaction directly, because that is what the robot
will have access to at test time. Further, training these representations to
then predict human actions not only gives them better structure, but also
enables them to be fine-tuned at test-time, when the robot observes the partner
act. this https URL.

本文主要研究在 assistive tasks 中应用 sim2real 技术时的问题，并提出了一种解决方案 —— 通过学习一种好的 latent representation 来匹配 test-time humans 能够准确映射到的人类策略，并且在 test-time 进行 fine-tune，这样能够更好地结构化人类策略。