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.

在本文中，我们提出了一种在共享的人机表示空间中建模社交动作预测的方法，通过这种方法我们能够合成与人类在社交场景中相互作用的机器人动作，即使在运动训练中没有观察到任何机器人。我们开发了一种名为 ECHO 的基于 Transformer 的架构，在上述共享空间中操作来预测社交场景中遇到的个体未来动作。与先前的工作相反，我们重新制定了社交动作问题，将其视为根据周围的个体来改进预测的单个动作的问题，这有助于训练，并且在场景中只有一个人时允许进行单个动作预测。我们在多人和人机动作预测任务中评估了我们的模型，并且取得了领先的性能，而且具有高效和实时执行的特点。此外，我们的定性结果展示了我们的方法通过文本命令生成可控制的人机交互行为的有效性。