text: 10,134,1446162932945985565,1203016782178443264,Jacob Krantz,"How does the choice of action space affect language-guided embodied navigators? 🤖 In our new paper ""Waypoint Models for Instruction-guided Navigation in Continuous Environments"", we discover implications for simulation and reality. 🧵 Oral at #iccv2021! <LINK> <LINK> We develop a class of highly configurable waypoint prediction networks to explore a spectrum of action spaces. We vary the ""expressivity"" from coarse-grained heading prediction up to continuous-valued waypoint prediction and train each model with large-scale RL. <LINK> We find that more expressive waypoint models result in simpler trajectories that are faster to execute on a real robot (by 2-3x!), but lower-level actions can better approximate shortest paths. Along the way, we set a new SotA on the VLN-CE leaderboard! <LINK> Project page: <LINK> A big thanks to my collaborators Aaron Gokaslan (@SkyLi0n), Dhruv Batra (@DhruvBatraDB), Stefan Lee (@stefmlee), and Oleksandr Maksymets (@o_maksymets).",https://arxiv.org/abs/2110.02207,"Little inquiry has explicitly addressed the role of action spaces in language-guided visual navigation -- either in terms of its effect on navigation success or the efficiency with which a robotic agent could execute the resulting trajectory. Building on the recently released VLN-CE setting for instruction following in continuous environments, we develop a class of language-conditioned waypoint prediction networks to examine this question. We vary the expressivity of these models to explore a spectrum between low-level actions and continuous waypoint prediction. We measure task performance and estimated execution time on a profiled LoCoBot robot. We find more expressive models result in simpler, faster to execute trajectories, but lower-level actions can achieve better navigation metrics by approximating shortest paths better. Further, our models outperform prior work in VLN-CE and set a new state-of-the-art on the public leaderboard -- increasing success rate by 4% with our best model on this challenging task. ","Waypoint Models for Instruction-guided Navigation in Continuous