  Integrating robots into populated environments is a complex challenge that
requires an understanding of human social dynamics. In this work, we propose to
model social motion forecasting in a shared human-robot representation space,
which facilitates us to synthesize robot motions that interact with humans in
social scenarios despite not observing any robot in the motion training. We
develop a transformer-based architecture called ECHO, which operates in the
aforementioned shared space to predict the future motions of the agents
encountered in social scenarios. Contrary to prior works, we reformulate the
social motion problem as the refinement of the predicted individual motions
based on the surrounding agents, which facilitates the training while allowing
for single-motion forecasting when only one human is in the scene. We evaluate
our model in multi-person and human-robot motion forecasting tasks and obtain
state-of-the-art performance by a large margin while being efficient and
performing in real-time. Additionally, our qualitative results showcase the
effectiveness of our approach in generating human-robot interaction behaviors
that can be controlled via text commands. Webpage: https://evm7.github.io/ECHO/
