Prudence Pitch

13:12 09/10/2019 |

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Facebook AI Research to imbue two robotic Sawyer arms with the ability to select appropriate steps from a library to complete an objective

(Tech) At each timestep, their agent had to decide which skill to use and what arguments to use for it. Despite the complexity involved, the team says that their approach yielded improvements in learning efficiency, such that manipulation skills could be discovered within only a few hours of training.



The team’s key insight was that for many tasks, the learning process could be split into two parts: (1) learning a task schema and (2) learning a policy that chooses appropriate parameterizations for the different skills. They assert that this approach leads to faster learning, in part because data from different versions of a given task could be used to improve shared skills. Moreover, they say it allowed for the transfer of learned schemas among related tasks.

The researchers gave the aforementioned two robotic arms a generic library of skills such as twisting, lifting, and reaching, which they had to apply to several lateral lifting, picking, opening, and rotating tasks involving varying objects, geometries, and initial poses. The schemas were learned in MuJoCo (a simulation environment) by training with low-dimensional input data like geometric and proprioceptive features (joint positions, joint velocities, end effector pose), and then transferred to visual inputs both in simulation as well as in the real world.

During experiments, the Sawyer arms which were equipped with cameras and controlled by Facebook’s PyRobot open source robotics platform, were tasked with manipulating nine household objects (such as a rolling pin, soccer ball, glass jar, and T-wrench) that required two parallel-jaw grippers to interact with. Despite having to learn from raw visual images, they say that the system learned to manipulate most items using 2,000 skills with over 90% success in around 4-10 hours of training.


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