  Learning from Demonstration (LfD) is a promising approach to enable
Multi-Robot Systems (MRS) to acquire complex skills and behaviors. However, the
intricate interactions and coordination challenges in MRS pose significant
hurdles for effective LfD. In this paper, we present a novel LfD framework
specifically designed for MRS, which leverages visual demonstrations to capture
and learn from robot-robot and robot-object interactions. Our framework
introduces the concept of Interaction Keypoints (IKs) to transform the visual
demonstrations into a representation that facilitates the inference of various
skills necessary for the task. The robots then execute the task using
sensorimotor actions and reinforcement learning (RL) policies when required. A
key feature of our approach is the ability to handle unseen contact-based
skills that emerge during the demonstration. In such cases, RL is employed to
learn the skill using a classifier-based reward function, eliminating the need
for manual reward engineering and ensuring adaptability to environmental
changes. We evaluate our framework across a range of mobile robot tasks,
covering both behavior-based and contact-based domains. The results demonstrate
the effectiveness of our approach in enabling robots to learn complex
multi-robot tasks and behaviors from visual demonstrations.
