TIDEE: The Robot That Cleans Up Without Guidance
In order for robots to operate effectively, they need to be more than just obedient machines. They should be able to respond to deviations from the norm and understand context even with incomplete instructions. This requires embodied commonsense reasoning, which is crucial for robots to interact naturally in the real world.
However, the field of embodied commonsense thinking has been lagging behind in comparison to robots that can follow step-by-step instructions. That’s why the research team has developed TIDEE, an embodied agent that can clean up spaces it has never seen before without any guidance.
TIDEE is unique because it has the ability to scan a scene, identify misplaced objects, and then move them back to their proper locations with precision. It achieves this through the use of visual search networks, visual-semantic detectors, and an associative neural graph memory.
Researchers tested TIDEE using the AI2THOR simulation environment, and it successfully cleaned up chaotic surroundings without any prior exposure to the environment. In fact, TIDEE performed better than other models that lacked the commonsense priors.
So how does TIDEE work? It goes through three distinct steps to tidy up a room. First, it scans the area and detects any anomalies. Then, it moves to the object and grabs it. Next, TIDEE infers a probable receptacle for the object based on the scene graph and external graph memory.
TIDEE also uses a visual search network to explore the area and suggest possible locations for receptacles. It retains information about previously identified objects for navigation and tracking. Visual attributes of each object are collected, along with relational language features.
While TIDEE has its limitations, such as not considering open and closed states of items or their 3D posture, it still outperforms other models and shows promise for future research.
Overall, TIDEE is a remarkable robot that can clean up spaces it has never seen before with ease. Its ability to understand context and reason effectively makes it a valuable asset in the field of AI.
Don’t forget to check out the Paper, Project, Github, and CMU Blog for more information.