  Learning to solve precision-based manipulation tasks from visual feedback
using Reinforcement Learning (RL) could drastically reduce the engineering
efforts required by traditional robot systems. However, performing fine-grained
motor control from visual inputs alone is challenging, especially with a static
third-person camera as often used in previous work. We propose a setting for
robotic manipulation in which the agent receives visual feedback from both a
third-person camera and an egocentric camera mounted on the robot's wrist.
While the third-person camera is static, the egocentric camera enables the
robot to actively control its vision to aid in precise manipulation. To fuse
visual information from both cameras effectively, we additionally propose to
use Transformers with a cross-view attention mechanism that models spatial
attention from one view to another (and vice-versa), and use the learned
features as input to an RL policy. Our method improves learning over strong
single-view and multi-view baselines, and successfully transfers to a set of
challenging manipulation tasks on a real robot with uncalibrated cameras, no
access to state information, and a high degree of task variability. In a hammer
manipulation task, our method succeeds in 75% of trials versus 38% and 13% for
multi-view and single-view baselines, respectively.
