Swoosh! Rattle! Thump! - Actions that Sound
Truly intelligent agents need to capture the interplay of all their senses to build a rich physical understanding of their world. In robotics, we have seen tremendous progress in using visual and tactile perception; however we have often ignored a key sense: sound. This is primarily due to lack of data that captures the interplay of action and sound. In this work, we perform the first large-scale study of the interactions between sound and robotic action. To do this, we create the largest available sound-action-vision dataset with 15,000 interactions on 60 objects using our robotic platform Tilt-Bot. By tilting objects and allowing them to crash into the walls of a robotic tray, we collect rich four-channel audio information. Using this data, we explore the synergies between sound and action, and present three key insights. First, sound is indicative of fine-grained object class information, e.g., sound can differentiate a metal screwdriver from a metal wrench. Second, sound also contains information about the causal effects of an action, i.e. given the sound produced, we can predict what action was applied on the object. Finally, object representations derived from audio embeddings are indicative of implicit physical properties. We demonstrate that on previously unseen objects, audio embeddings generated through interactions can predict forward models 24% better than passive visual embeddings.
This paper presents audio-visual object classification and motion prediction work on a novel dataset of 60 different objects rolling around in a bin tilted to and fro by a robot, with video and 4-channel audio recordings of the object impacts. The data is rather novel, is large enough to do ML (around 17 hours of eventful audio/video) and is to be publicly released. The model architectures are not of theoretical novelty. However, the experiments are somewhat interesting. It was found that the audio contains significant object classification information. The audio was also good for predicting the trajectory of the object. This might not be surprising since the microphones are geometrically arranged and may contain directional information along with information about velocity and/or distance traveled. Overall the experiments are rather thin with only a few experimental results. A more thorough undertaking might be expected for ICLR papers, with more novel theoretical development and more extensive experiments.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Swoosh! Rattle! Thump! - Actions that Sound
Truly intelligent agents need to capture the interplay of all their senses to build a rich physical understanding of their world. In robotics, we have seen tremendous progress in using visual and tactile perception; however we have often ignored a key sense: sound. This is primarily due to lack of data that captures the interplay of action and sound. In this work, we perform the first large-scale study of the interactions between sound and robotic action. To do this, we create the largest available sound-action-vision dataset with 15,000 interactions on 60 objects using our robotic platform Tilt-Bot. By tilting objects and allowing them to crash into the walls of a robotic tray, we collect rich four-channel audio information. Using this data, we explore the synergies between sound and action, and present three key insights. First, sound is indicative of fine-grained object class information, e.g., sound can differentiate a metal screwdriver from a metal wrench. Second, sound also contains information about the causal effects of an action, i.e. given the sound produced, we can predict what action was applied on the object. Finally, object representations derived from audio embeddings are indicative of implicit physical properties. We demonstrate that on previously unseen objects, audio embeddings generated through interactions can predict forward models 24% better than passive visual embeddings.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



This paper presents audio-visual object classification and motion prediction work on a novel dataset of 60 different objects rolling around in a bin tilted to and fro by a robot, with video and 4-channel audio recordings of the object impacts. The data is rather novel, is large enough to do ML (around 17 hours of eventful audio/video) and is to be publicly released. The model architectures are not of theoretical novelty. However, the experiments are somewhat interesting. It was found that the audio contains significant object classification information. The audio was also good for predicting the trajectory of the object. This might not be surprising since the microphones are geometrically arranged and may contain directional information along with information about velocity and/or distance traveled. Overall the experiments are rather thin with only a few experimental results. A more thorough undertaking might be expected for ICLR papers, with more novel theoretical development and more extensive experiments.
assistant
This paper presents audio-visual object classification and motion prediction work on a novel dataset of 60 different objects rolling around in a bin tilted to and fro by a robot, with video and 4-channel audio recordings of the object impacts. The data is rather novel, is large enough to do ML (around 17 hours of eventful audio/video) and is to be publicly released. The model architectures are not of theoretical novelty. However, the experiments are somewhat interesting. It was found that the audio contains significant object classification information. The audio was also good for predicting the trajectory of the object. This might not be surprising since the microphones are geometrically arranged and may contain directional information along with information about velocity and/or distance traveled. Overall the experiments are rather thin with only a few experimental results. A more thorough undertaking might be expected for ICLR papers, with more novel theoretical development and more extensive experiments.