Teaching Perception
This addresses the challenge of flexible perception for robotics, though it appears incremental as it builds on existing symbolic reasoning methods.
The paper tackles the problem of enabling robots to adapt their perception based on verbal instructions rather than hardwired decisions, demonstrating a fully implemented system that guides a physical robot's perception in simple scenarios.
The visual world is very rich and generally too complex to perceive in its entirety. Yet only certain features are typically required to adequately perform some task in a given situation. Rather than hardwire-in decisions about when and what to sense, this paper describes a robotic system whose behavioral policy can be set by verbal instructions it receives. These capabilities are demonstrated in an associated video showing the fully implemented system guiding the perception of a physical robot in simple scenario. The structure and functioning of the underlying natural language based symbolic reasoning system is also discussed.