text:   Environments",4,"['How does the choice of action space affect language-guided embodied navigators? 🤖\n\nIn our new paper ""Waypoint Models for Instruction-guided Navigation in Continuous Environments"", we discover implications for simulation and reality. 🧵\n\nOral at #iccv2021!\n<LINK> <LINK>', 'We develop a class of highly configurable waypoint prediction networks to explore a spectrum of action spaces.\n\nWe vary the ""expressivity"" from coarse-grained heading prediction up to continuous-valued waypoint prediction and train each model with large-scale RL. https://t.co/1PlM2RhdXA', '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.\n\nAlong the way, we set a new SotA on the VLN-CE leaderboard! https://t.co/cyOQF1GLxz', 'Project page: https://t.co/yBK76qERDw\n\nA big thanks to my collaborators Aaron Gokaslan (@SkyLi0n), Dhruv Batra (@DhruvBatraDB), Stefan Lee (@stefmlee), and Oleksandr Maksymets (@o_maksymets).']",21,10,971