Physical practice is the essence of embodied intelligence. Physical simulators construct high-fidelity training environments, while world models provide the internal characteristics of the environment. The integration of these three elements enables the creation of rich, effective and realistic environments for training embodied intelligent robots in perceptual and cognitive abilities regarding both contact and non-contact interactions with the environment, laying a solid foundation for decision-making and control.

TREND 2 Empowering Embodied Decision-Making via Multimodal Large Model

Multi-level end-to-end embodied decision-making: Inspired by multimodal large models,  the research on cognition and planning with mathematical and physical foundations, integrated with life sciences and combined with real-time control modules, significantly enhances the task generalization ability of embodied intelligent robots in unstructured environments.

TREND 3 Embodied Intelligent Control via Integrating Model Prediction, Reinforcement Learning and Life Sciences

By integrating the dynamic optimization capabilities of model predictive control, adaptive strategies of reinforcement learning, and the redundant multi-loop control mechanisms from life sciences, an embodied intelligent robot control system is constructed to improve the generalization and adaptability of embodied intelligent robot control in dynamic environments.

TREND 4 Generative AI for Embodied Intelligent Robot Design

Generative AI-driven intelligent robot design realizes unified optimization of motors, reducers, drivers, structures, connectors, and materials. In combination with research and development technologies of robotic structure, it achieves co-optimization of hardware and control strategies in physical  simulators, enabling automatic exploration of task-optimal embodied intelligent robot designs.

TREND 5 Highly Synergistic and Dynamically Adaptive Software/Hardware Consistency for Embodied Intelligence

Embodied intelligent robots require consistent software/hardware co-design. By predefining interface specifications of algorithms during hardware development and embedding physical constraints of hardware into algorithm design, system-level consistency and optimization are achieved through joint simulation and validation.

TREND 6  “Manufactory” of Embodied Intelligent Robots

In the simulation environment, research and development efforts such as natural language interaction, environment generation, robot body design, decision-control algorithms, and software-hardware consistency algorithms are integrated into an organic whole, enabling  rapid design and manufacturing of high-quality embodied intelligent robot systems according to specific performance and requirements.

TREND 7 Large-Scale and High-Quality Dataset for Embodied Intelligence

The construction of large-scale and high-quality datasets for embodied intelligence through physical entity collection and simulation synthesis can enhance embodied intelligent robots’ capabilities in morphological optimization, multimodal training efficiency, and cross-scenario policy transfer.

TREND 8 Advances in Embodied Intelligent Robot Swarms and the Collaboration with Humans

Integrating multi-agent coordination mechanisms to construct embodied intelligent robot swarms, while continuously improving the safety of embodied intelligent agents and their ability to empathize with humans, will facilitate such robots’ entry into human society and realize their integration with humans.

TREND 9 Interdisciplinary Open Community for Embodied Intelligent Robots

The development of embodied intelligent robots requires collaboration across multiple disciplines, such as information sciences, engineering and materials sciences, mathematical and physical sciences, and life sciences. This initiative will bring together experts  and scholars worldwide to promote technical discussions in the field of embodied intelligence, and facilitate in-depth integration and collaborative development throughout the industrial chain.

TREND 10 Safety Assessment and Ethical Development for Embodied Intelligent Robots

Through behavior norm verification, decision interpretability analysis, and data security research, a safety assessment framework and ethical norms for embodied intelligent robots will be established to ensure decision reliability and behavioral safety in complex and open environments.