Learning Adaptive Sensorimotor Representations

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A core competency of any intelligent physical system is its ability to operate successfully in a wide range of environments and to adapt autonomously to changes in task conditions. Current state-of-the-art systems for robot perception and control are increasingly able to overcome challenging environmental factors, typically through the use of "big-data" and machine learning techniques, but these systems require extensive human engineering and data annotation and are typically not flexible enough to overcome task variations without hands-on interaction from their human designer. My research will focus on what I believe to be a core challenge of intelligent robotics - the automated learning of representations that link a robot's sensors and actuators to achieve adaptive solutions for perception and control tasks. I believe this can be accomplished by combining the state-of-the-art in learning visual representations (e.g., deep representations now used for recognizing objects) and in adaptive control (e.g., policy-gradient approaches now used to learn motor skills). The key assumption of this research direction is that an automated intermediate representation will more flexibly capture the correlations between perception and control than those chosen by a human designer. The key advantage, if the research is successful, will be robots that require drastically reduced setup and training to perform new tasks successfully. A motivational example for this work is an autonomous robotic chef. Today, we can feasibly produce robots to cook a small variety of meals in a specially-designed kitchen. One can visit Japan to find examples of this technology. However, it would require enormous effort to produce a robot that could cook even a single meal in a kitchen it had not seen before. The main challenge would be the robot's inability to generalize its knowledge to differences in the placement of items and their properties (e.g., my knife is slightly smaller and is kept in a different drawer). This research program will improve a robot's ability to handle new environments with a reduced amount of effort from human engineers. Beyond cooking, we will target applications to society, such as disaster rescue, and industry, such as mining and agriculture.