Autonomous physics-based object manipulation

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Planning of object manipulation is an important problem in robotics. Imagine an autonomous home-helper robot reaching into a cluttered fridge shelf: the robot will need to make contact with multiple objects and push them out the way. The robot's actions must be carefully planned to avoid pushing objects off the shelf, causing them to fall down and break. Existing algorithms solve this planning problem by searching over possible sequences of actions. Each action is applied for a discrete time step, and the planner chooses the sequence which is predicted to reach the goal object without damaging other objects in the scene. The effect of each action during a time step is computed using a physics simulation, which can either be a complete dynamics simulation or a simplified quasi-static model. However, a major problem with existing algorithms is speed: The number of possible action sequences increases exponentially with the planning horizon, leading to unacceptably long planning times. New methods that reduce planning time can have a major impact in this area, enabling robots to perform manipulation tasks that currently exceed their capabilities. In this project we will develop new optimisation based approaches to autonomous physics-based object manipulation in clutter.