Hierarchical Trajectory Planning Method for Piano-Playing Robot

Piano-playing tasks, which effectively demonstrate bimanual coordination capabilities in humanoid robots, are increasingly becoming a research focus. However, prior research has predominantly focused on Cartesian space trajectory planning without adequately addressing real-world obstacle avoidance constraints and manipulator acceleration limits. This paper proposes a hierarchical trajectory planning framework that systematically incorporates both obstacle avoidance and acceleration constraints. Firstly, discrete Cartesian path points are generated using a dynamic programming approach; secondly, joint space path points are derived considering obstacle avoidance and joint limit constraints through dynamic programming; thirdly, the joint space trajectory is interpolated using a Jacobian inverse-based method; finally, the trajectory is refined using Model Predictive Control (MPC). Experimental results demonstrate that the proposed method produces trajectories satisfying both obstacle avoidance and acceleration constraints, enabling fluent piano piece execution in real-world environments.

Authors

• Zirui Wang
• Jiayu Zhang
• Wei Jiang
• Tao Jiang
• Jingdong Zhao
• Liangliang Zhao
• Baoshi Cao
• Le Qi
• Yuchen Yang
• Fenglei Ni
• Hong Liu 0002

Keywords

• Jacobian matrices
• Interpolation
• Trajectory planning
• Humanoid robots
• Aerospace electronics
• Trajectory
• Dynamic programming
• Collision avoidance
• Manipulator dynamics
• Videos
• Model Predictive Control
• Joint Space
• Obstacle Avoidance
• Cartesian Space
• Humanoid Robot
• Joint Limits
• Dynamic Programming Approach
• Path Points
• Time Step
• Transition State
• Left Hand
• Optimal Control
• Minimum Distance
• Joint Angles
• Robotic Arm
• Null Space
• Coordinate Frame
• Joint Acceleration
• Collision Detection
• Joint Velocity
• Joint Trajectories
• Continuous Trajectory
• Imitation Learning
• Little Finger
• Base Frame
• Dynamic Programming Method
• Cost Matrix
• Piano-playing
• trajectory generation
• Model Predictive Control(MPC)