  We study how the choice of visual perspective affects learning and
generalization in the context of physical manipulation from raw sensor
observations. Compared with the more commonly used global third-person
perspective, a hand-centric (eye-in-hand) perspective affords reduced
observability, but we find that it consistently improves training efficiency
and out-of-distribution generalization. These benefits hold across a variety of
learning algorithms, experimental settings, and distribution shifts, and for
both simulated and real robot apparatuses. However, this is only the case when
hand-centric observability is sufficient; otherwise, including a third-person
perspective is necessary for learning, but also harms out-of-distribution
generalization. To mitigate this, we propose to regularize the third-person
information stream via a variational information bottleneck. On six
representative manipulation tasks with varying hand-centric observability
adapted from the Meta-World benchmark, this results in a state-of-the-art
reinforcement learning agent operating from both perspectives improving its
out-of-distribution generalization on every task. While some practitioners have
long put cameras in the hands of robots, our work systematically analyzes the
benefits of doing so and provides simple and broadly applicable insights for
improving end-to-end learned vision-based robotic manipulation.
