Following the Human Thread in Social Navigation
This addresses the challenge of real-time human-robot collaboration in shared environments, though it is incremental as it builds on existing social navigation methods.
The paper tackles the problem of robots adapting to human motion in social navigation by introducing the Social Dynamics Adaptation (SDA) model, which uses a two-stage reinforcement learning framework to infer social dynamics from history and achieves state-of-the-art performance in finding and following humans on the Habitat 3.0 platform.
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 present 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 (SotA) performance in finding and following humans. The code can be found at https://github.com/L-Scofano/SDA.