Stanford researchers develop a robot training system that uses audio feedback for effective task execution.
Unlike expensive and fragile tactile sensors, audio sensors are cheap, robust, and easily integrated into existing systems. A team of Stanford researchers has developed a system to train robots using audio, which helps them to execute tasks effectively.
Audio signals provide valuable details about contact events, modes, surface materials, and object states. Unlike expensive and fragile tactile sensors, audio sensors are cheap, robust, and easily integrated into existing systems. The hardware includes a portable hand-held device with an “ear-in-hand” design for in-the-wild data collection. This device provides both vision and audio feedback. The setup captures high-quality contact audio with a piezoelectric microphone.
Researchers found that using a contact microphone significantly improves robot sensitivity to different surface materials. In tests, the vision-only policy had a 20 percent success rate, and using an environment microphone yielded similar results, failing to detect subtle differences.to pick up the correct tape. Additionally, training-time noise augmentation proved more effective than test-time noise reduction.
Humanoid Robot Robot Training Stanford University
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