Whole Body Trajectory

Whole-body trajectory optimization focuses on generating efficient and dynamically feasible motion plans for robots, considering the complex interplay of all body segments and environmental interactions. Current research emphasizes robust methods, often combining model-based optimization techniques like differential dynamic programming with reinforcement learning or model predictive control, to handle high-dimensionality, uncertainties, and real-time constraints. These advancements are crucial for enabling agile locomotion, dexterous manipulation, and safe operation in unstructured environments for robots across various platforms, from humanoid and legged robots to mobile manipulators. The resulting improvements in robot control and planning have significant implications for applications in robotics, including planetary exploration, assistive technologies, and industrial automation.