  Furniture assembly remains an unsolved problem in robotic manipulation due to
its long task horizon and nongeneralizable operations plan. This paper presents
the Tactile Ensemble Skill Transfer (TEST) framework, a pioneering offline
reinforcement learning (RL) approach that incorporates tactile feedback in the
control loop. TEST's core design is to learn a skill transition model for
high-level planning, along with a set of adaptive intra-skill goal-reaching
policies. Such design aims to solve the robotic furniture assembly problem in a
more generalizable way, facilitating seamless chaining of skills for this
long-horizon task. We first sample demonstration from a set of heuristic
policies and trajectories consisting of a set of randomized sub-skill segments,
enabling the acquisition of rich robot trajectories that capture skill stages,
robot states, visual indicators, and crucially, tactile signals. Leveraging
these trajectories, our offline RL method discerns skill termination conditions
and coordinates skill transitions. Our evaluations highlight the proficiency of
TEST on the in-distribution furniture assemblies, its adaptability to unseen
furniture configurations, and its robustness against visual disturbances.
Ablation studies further accentuate the pivotal role of two algorithmic
components: the skill transition model and tactile ensemble policies. Results
indicate that TEST can achieve a success rate of 90\% and is over 4 times more
efficient than the heuristic policy in both in-distribution and generalization
settings, suggesting a scalable skill transfer approach for contact-rich
manipulation.
