Unlike human beings that can employ the entire surface of their limbs as a
means to establish contact with their environment, robots are typically
programmed to interact with their environments via their end-effectors, in a
collision-free fashion, to avoid damaging their environment. In a departure
from such a traditional approach, this work presents a contact-aware controller
for reference tracking that maintains interaction forces on the surface of the
robot below a safety threshold in the presence of both rigid and soft contacts.
Furthermore, we leveraged the proposed controller to extend the BiTRRT
sample-based planning method to be contact-aware, using a simplified contact
model. The effectiveness of our framework is demonstrated in hardware
experiments using a Franka robot in a setup inspired by the Amazon stowing
task. A demo video of our results can be seen here:
https://youtu.be/2WeYytauhNgComment: RSS 2023. Workshop: Experiment-oriented Locomotion and Manipulation
Researc