The field of robotics and swarm intelligence is witnessing significant advancements, with a focus on developing adaptive and lifelong learning capabilities. Researchers are exploring innovative approaches to enable robots and swarms to learn from experience, adapt to new tasks and environments, and retain knowledge over time. One of the key directions is the development of transferable latent-to-latent policies, which allow robots to leverage prior experience and accelerate adaptation to new tasks and entities. Another area of focus is the evolution of swarm controllers, which can preserve information about previous tasks and reuse it to foster adaptation and mitigate forgetting. Additionally, contrastive learning and teacher-student frameworks are being employed to improve the robustness and generalization of quadrupedal locomotion control. Noteworthy papers in this area include:

• Transferable Latent-to-Latent Locomotion Policy for Efficient and Versatile Motion Control of Diverse Legged Robots, which proposes a latent training framework for efficient adaptation to new robot entities and tasks.
• TAR: Teacher-Aligned Representations via Contrastive Learning for Quadrupedal Locomotion, which leverages privileged information with self-supervised contrastive learning to improve generalization to Out-of-Distribution scenarios.