  Task and motion planning (TAMP) algorithms have been developed to help robots
plan behaviors in discrete and continuous spaces. Robots face complex
real-world scenarios, where it is hardly possible to model all objects or their
physical properties for robot planning (e.g., in kitchens or shopping centers).
In this paper, we define a new object-centric TAMP problem, where the TAMP
robot does not know object properties (e.g., size and weight of blocks). We
then introduce Task-Motion Object-Centric planning ({\bf TMOC}), a grounded
TAMP algorithm that learns to ground objects and their physical properties with
a physics engine. TMOC is particularly useful for those tasks that involve
dynamic complex robot-multi-object interactions that can hardly be modeled
beforehand. We have demonstrated and evaluated TMOC in simulation and using a
real robot. Results show that TMOC outperforms competitive baselines from the
literature in cumulative utility.
