  The success of collaboration between humans and robots in shared environments
relies on the robot's real-time adaptation to human motion. Specifically, in
Social Navigation, the agent should be close enough to assist but ready to back
up to let the human move freely, avoiding collisions. Human trajectories emerge
as crucial cues in Social Navigation, but they are partially observable from
the robot's egocentric view and computationally complex to process.
  We propose the first Social Dynamics Adaptation model (SDA) based on the
robot's state-action history to infer the social dynamics. We propose a
two-stage Reinforcement Learning framework: the first learns to encode the
human trajectories into social dynamics and learns a motion policy conditioned
on this encoded information, the current status, and the previous action. Here,
the trajectories are fully visible, i.e., assumed as privileged information. In
the second stage, the trained policy operates without direct access to
trajectories. Instead, the model infers the social dynamics solely from the
history of previous actions and statuses in real-time. Tested on the novel
Habitat 3.0 platform, SDA sets a novel state of the art (SoA) performance in
finding and following humans.
