We present a robotic setup for real-world testing and evaluation of
human-robot and human-human collaborative learning. Leveraging the
sample-efficiency of the Soft Actor-Critic algorithm, we have implemented a
robotic platform able to learn a non-trivial collaborative task with a human
partner, without pre-training in simulation, and using only 30 minutes of
real-world interactions. This enables us to study Human-Robot and Human-Human
collaborative learning through real-world interactions. We present preliminary
results, showing that state-of-the-art deep learning methods can take
human-robot collaborative learning a step closer to that of humans interacting
with each other.