Robots Face Challenges in Basic Tasks Despite Advances in Embodied Intelligence
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Over the past year, the robotics industry has engaged in a competitive race focused on enhancing the computational power, parameters, and algorithms of robotic 'brains.' While advancements in reasoning capabilities are evident, robots still struggle with basic tasks such as grasping objects or performing precise manipulations. This discrepancy raises questions about the effectiveness of current sensory technologies.
The core issue lies in the limitations of robotic perception, which relies heavily on either pure vision or multi-sensor fusion approaches. Multi-sensor fusion, favored by many embodied intelligence manufacturers, combines various sensors to improve robustness and accuracy. However, this method introduces challenges related to data synchronization and processing overhead, hindering the scalability of embodied intelligence.
Conversely, pure vision systems, exemplified by Tesla's approach, depend on 2D RGB cameras to reconstruct 3D environments. This method lacks depth information and can falter in challenging visual conditions. Both approaches suffer from the loss of information during data transmission and processing, resulting in robots receiving 'second-hand data' rather than real-time, unified information from the physical world. No further timeline was disclosed at the time of publication.
Original report
Despite Advances in Embodied Intelligence, Robots Struggle with Basic Tasks
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