The recent advancements in the field of AI and robotics have seen a significant shift towards integrating physical principles with neural network learning. This approach aims to enhance the accuracy and interpretability of AI models by leveraging the strengths of both traditional physics-based simulations and modern neural network techniques. Specifically, there is a growing emphasis on developing hybrid models that can learn and adapt to complex, real-world dynamics, such as particle interactions and robot control, by combining physical priors with learned corrections. These models not only improve the accuracy of simulations but also enhance their generalizability and robustness across various scenarios. Additionally, the use of differentiable rendering techniques is emerging as a powerful tool for bridging the gap between visual data and robotic control, enabling more intuitive and effective robot manipulation through vision-language models. Notably, the integration of evolutionary methods for post-training model refinement is also gaining traction, offering a versatile and efficient approach to optimizing models with non-differentiable objectives, such as human evaluations and threshold-based criteria.

Noteworthy Developments:

• The Neural Material Adaptor (NeuMA) and Particle-GS offer a novel approach to visual grounding of intrinsic dynamics by integrating physical laws with learned corrections.
• DEL: Discrete Element Learner demonstrates significant improvements in learning 3D particle dynamics from 2D images by incorporating learnable graph kernels into a mechanics-integrated framework.
• AfterLearnER introduces a versatile method for refining fully-trained models using non-differentiable optimization, requiring minimal feedback and showing limited overfitting.
• Differentiable Robot Rendering bridges the modality gap in robotics by making robot appearance directly differentiable with respect to control parameters, enabling effective gradients for robotic control from visual data.