  We present a learning-based force-torque dynamics to achieve model-based
control for contact-rich peg-in-hole task using force-only inputs. Learning the
force-torque dynamics is challenging because of the ambiguity of the
low-dimensional 6-d force signal and the requirement of excessive training
data. To tackle these problems, we propose a multi-pose force-torque state
representation, based on which a dynamics model is learned with the data
generated in a sample-efficient offline fashion. In addition, by training the
dynamics model with peg-and-holes of various shapes, scales, and elasticities,
the model could quickly transfer to new peg-and-holes after a small number of
trials. Extensive experiments show that our dynamics model could adapt to
unseen peg-and-holes with 70% fewer samples required compared to learning from
scratch. Along with the learned dynamics, model predictive control and
model-based reinforcement learning policies achieve over 80% insertion success
rate. Our video is available at https://youtu.be/ZAqldpVZgm4.
