The field of bipedal locomotion is moving towards more efficient and robust control approaches, with a focus on descriptive models and heuristic step planning. Researchers are exploring novel control frameworks that avoid prescribing low-dimensional models to full models, instead using descriptive models with minimal degrees of freedom to maintain balance. This allows for more human-like walking gaits and improved robustness. Additionally, there is a trend towards incorporating reinforcement learning and model-based predictive control to enable smooth transitions across diverse terrains. Noteworthy papers include:

• A novel control approach that uses a descriptive model with minimal degrees of freedom to maintain balance, resulting in efficient human-like walking gaits and improved robustness.
• A comparative study of model-based and model-free approaches for learning dynamic bipedal locomotion, demonstrating the effectiveness of heuristic step planning.
• The development of a multi-modal bipedal robot that can walk, crawl, climb, and roll, highlighting the importance of tight integration between morphology, high-level planning, and control.