  Learning socially-aware motion representations is at the core of recent
advances in multi-agent problems, such as human motion forecasting and robot
navigation in crowds. Despite promising progress, existing representations
learned with neural networks still struggle to generalize in closed-loop
predictions (e.g., output colliding trajectories). This issue largely arises
from the non-i.i.d. nature of sequential prediction in conjunction with
ill-distributed training data. Intuitively, if the training data only comes
from human behaviors in safe spaces, i.e., from "positive" examples, it is
difficult for learning algorithms to capture the notion of "negative" examples
like collisions. In this work, we aim to address this issue by explicitly
modeling negative examples through self-supervision: (i) we introduce a social
contrastive loss that regularizes the extracted motion representation by
discerning the ground-truth positive events from synthetic negative ones; (ii)
we construct informative negative samples based on our prior knowledge of rare
but dangerous circumstances. Our method substantially reduces the collision
rates of recent trajectory forecasting, behavioral cloning and reinforcement
learning algorithms, outperforming state-of-the-art methods on several
benchmarks. Our code is available at https://github.com/vita-epfl/social-nce.
