  Automating dexterous, contact-rich manipulation tasks using rigid robots is a
significant challenge in robotics. Rigid robots, defined by their actuation
through position commands, face issues of excessive contact forces due to their
inability to adapt to contact with the environment, potentially causing damage.
While compliance control schemes have been introduced to mitigate these issues
by controlling forces via external sensors, they are hampered by the need for
fine-tuning task-specific controller parameters. Learning from Demonstrations
(LfD) offers an intuitive alternative, allowing robots to learn manipulations
through observed actions. In this work, we introduce a novel system to enhance
the teaching of dexterous, contact-rich manipulations to rigid robots. Our
system is twofold: firstly, it incorporates a teleoperation interface utilizing
Virtual Reality (VR) controllers, designed to provide an intuitive and
cost-effective method for task demonstration with haptic feedback. Secondly, we
present Comp-ACT (Compliance Control via Action Chunking with Transformers), a
method that leverages the demonstrations to learn variable compliance control
from a few demonstrations. Our methods have been validated across various
complex contact-rich manipulation tasks using single-arm and bimanual robot
setups in simulated and real-world environments, demonstrating the
effectiveness of our system in teaching robots dexterous manipulations with
enhanced adaptability and safety.
