In order for robots to function in everyday life environments, from flexible manufacturing and warehouse domains to households, they need to autonomously grasp and manipulate a wide variety of potentially unknown objects. Currently, autonomous robots are practically unable to work outside highly-controlled environments wherein an accurate model of every object is provided. This limitation is partially due to the lack of robust algorithms for grasping and manipulating objects with unknown geometric or mechanical properties. The proposed project will perform fundamental research into robust robotic manipulation in a way that will enable autonomous robots to interact efficiently with a large variety of everyday physical objects for extended periods of time. The objective is for autonomous robotic manipulators to effectively learn from experience how objects may physically interact with each other and with the robotic arm. The next step is to utilize this experience so as to perform robust manipulation tasks. There are many exemplary robotic tasks that can be benefited from the proposed improvements and which will form the basis of the project's experimentation process. They include the pushing of objects to desired poses, reconfiguration of objects to simplify their picking and the handling of tools.
The project develops three key components: (1) An algorithm for learning inertial, elastic, and friction properties of an unknown object by observing how the object moves when manipulated by a robot. The project will research novel Bayesian optimization techniques for black-box system identification in order to learn probabilistic models of objects. (2) A physically realistic simulator that can provide a stochastic model of an object's motion given the physical parameters learned by the first component. This will be achieved by utilizing online non-parametric learning methods for speeding up physically realistic simulations under uncertainty. (3), A robust planning algorithm that utilizes the simulator for finding optimal actions to apply on the object given the learned stochastic model. The objective is to converge to increasingly robust solutions as computation time increases and the robot acquires increased experience with objects in an environment. To strengthen the project's broader impact, the PIs will provide implementations of their solutions to the research community as open-source software packages. This will be coupled with the generation of educational material, which will aim to attract undergraduate students early in their studies to STEM. The PIs will also aim to organize academic meetings that will bring together researchers from foundational domains, robotics experts and industry representatives.
