Envisioning the qualitative effects of robot manipulation actions using simulation-based projections

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The limits and potentials of deep learning for robotics (2018, background): “These model-based techniques often capture properties such as the mass, momentum, shape, and surface friction of objects, and use these to generate controls that change the environment in a desirable way [57], [59], [105], [119].”
Visual closed-loop control for pouring liquids (2016, background): “There is prior work related to robotic pouring, however, most of it either uses coarse simulations disconnected from real liquid perception and dynamics [9], [5] or constrained task spaces that bypass the need to perceive and reason directly about liquids [10], [11], [12], [13], [14].”
Reasoning About Liquids via Closed-Loop Simulation (2017, background): “However, in [10, 11, 34, 33] they do not simulate liquids; rather they use small, rigid spheres as a stand-in for liquid.”