[T-RO24] Tac-Man: Tactile-Informed Prior-Free Manipulation of Articulated Objects
Abstract
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. Advancements in tactile-informed approaches significantly expand the scope of robotic applications in human-centric environments, particularly where accurate models are difficult to obtain.
Zihang Zhao
Ph.D. '22
My research interests include robotics, mechatronics, and tactility-related robot cognition, etc.
Yuyang Li
Ph.D. '24
My research interests lie in the intersection of 3D computer vision, computer graphics, and robotics. My long-term goal is to create intelligence that perceives, understands, and interacts with the physical / virtual environments.
Lecheng Ruan
Research Professor
