  Integrating robotics into human-centric environments such as homes,
necessitates advanced manipulation skills as robotic devices will need to
engage with articulated objects like doors and drawers. Key challenges in
robotic manipulation are the unpredictability and diversity of these objects'
internal structures, which render models based on priors, both explicit and
implicit, inadequate. Their reliability is significantly diminished by
pre-interaction ambiguities, imperfect structural parameters, encounters with
unknown objects, and unforeseen disturbances. Here, we present a prior-free
strategy, Tac-Man, focusing on maintaining stable robot-object contact during
manipulation. Utilizing tactile feedback, but independent of object priors,
Tac-Man enables robots to proficiently handle a variety of articulated objects,
including those with complex joints, even when influenced by unexpected
disturbances. Demonstrated in both real-world experiments and extensive
simulations, it consistently achieves near-perfect success in dynamic and
varied settings, outperforming existing methods. Our results indicate that
tactile sensing alone suffices for managing diverse articulated objects,
offering greater robustness and generalization than prior-based approaches.
This underscores the importance of detailed contact modeling in complex
manipulation tasks, especially with articulated objects. Advancements in
tactile sensors significantly expand the scope of robotic applications in
human-centric environments, particularly where accurate models are difficult to
obtain.
