Embodied intelligence is seen as an important path to artificial general intelligence. However, there are still many challenges in perception and cognition, learning and transfer, computing power, multi-task collaboration, security assurance, privacy maintenance, and human-computer interaction.
Technical challenges
Perceptual and cognitive aspects
Difficulty in multimodal perception fusion: Humans perceive the world through multiple senses, and the visual, auditory, tactile and other sensor data formats and characteristics in the embodied intelligent system are different.
Insufficient environment understanding and semantic parsing: Embodied intelligence needs to understand the semantic information of various objects, scenes, and events in the environment, such as the function of recognizing objects, understanding the purpose of the scene, and predicting the development of events. Current algorithms have limited ability to parse semantic information in complex environments, and it is difficult to reach the depth and breadth of human understanding.
Learning and generalization
Data acquisition and annotation challenges: Embodied intelligence systems require large amounts of data to train models, but real-world data collection is expensive and difficult to cover all possible scenarios and situations. At the same time, data annotation also requires a lot of manpower and time, and how to efficiently obtain and annotate high-quality data is a key challenge.
Sample imbalance: In practical applications, embodied intelligence may encounter a small amount of data in some scenarios or tasks and a large amount of data in other scenarios or tasks, which will lead to insufficient learning of a small number of samples during training, thus affecting its generalization ability in these scenarios.
Limitations of reinforcement learning: Although reinforcement learning is a commonly used learning method in embodied intelligence, it requires a lot of trial and error in the training process, has low learning efficiency, and is easy to fall into local optimal solutions. In addition, the performance and stability of reinforcement learning algorithms need to be improved in the face of complex and long-term tasks.
Figure: Technical challenges faced by embodied intelligence
Decision-making and control
High requirements for real-time decision-making: Embodied intelligence systems need to make decisions quickly when interacting with the environment, such as in autonomous driving scenarios, where the vehicle needs to react to unexpected situations in a short period of time. This requires the algorithm to be able to process a large amount of perceptual information in a limited time and generate reasonable decisions, which puts forward high requirements for the real-time and computational efficiency of the algorithm.
The refinement of motion control is insufficient: human motion control is very flexible and fine, while the existing embodied intelligent system is still relatively rough in motion control, and it is difficult to achieve high-precision and smooth movements like humans. For example, when robots perform complex operation tasks, they may have problems such as uncoordinated movements and inaccurate force control, which requires more advanced control algorithms to improve the fineness and stability of action control.
Complex task planning and scheduling: When the embodied intelligent system is faced with multiple tasks or goals, reasonable task planning and scheduling are required to optimize resource allocation and improve the overall performance of the system. However, task planning and scheduling problems in complex environments are often NP-hard problems, and it is difficult to find the optimal solution, and it is necessary to develop effective heuristic algorithms or approximate algorithms to solve them.
Model architecture and optimization
Lack of a common model architecture: At present, there is no widely recognized general model architecture in the field of embodied intelligence, and different application scenarios and tasks often require the design of specialized models, which increases the difficulty and cost of algorithm development. The development of a general model architecture that can be applied to a variety of embodied intelligence tasks is an important research direction in this field.
Contradiction between model complexity and computing resources: In order to improve the performance of embodied intelligent systems, complex deep learning models are often used, but these models often require large amounts of computing resources for training and inference. In practical applications, embodied smart devices may be limited by hardware resources, such as computing power, memory, and energy, and how to find a balance between model complexity and computing resources is a problem that needs to be solved in algorithm design.
Poor interpretability of models: Deep learning models are often a black box with an incomprehensible decision-making process and basis, which is detrimental to the safety and reliability of embodied intelligence systems. For example, in critical areas such as healthcare, transportation, etc., people need to understand why the system makes decisions so that they can be evaluated and trusted. Therefore, improving the interpretability of the model is also one of the challenges faced by embodied intelligence algorithms.
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