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Method Overview

- (left) Before solving the reach-avoid game, we specify the target set (goal locations), failure set (collisions), and a conditional behavior prediction (CBP) model that can predict the human's future trajectory conditioned on the robot's future plan. (center) During simulated gameplay, the SLIDE policy, π*(xe), is trained against a simulated human adversary π(xe) whose control bounds are informed by the CBP model. (right) Online, the robot uses its robust SLIDE policy to safely influence against any> human. + (left) Before solving the reach-avoid game, we specify the target set (goal locations), failure set (collisions), and a conditional behavior prediction (CBP) model that can predict the human's future trajectory conditioned on the robot's future plan. (center) During simulated gameplay, the SLIDE policy, π*(xe), is trained against a simulated human adversary π(xe) whose control bounds are informed by the CBP model. (right) Online, the robot uses its robust SLIDE policy to safely influence against any human.