Optimal Control Approach for Non-prehensile Ball Juggling Using a 7-DoF Manipulator

Non-prehensile object manipulation skills are important for real-world robot interactions, enabling highly dynamic tasks such as balancing a glass on a tray or the controlled sliding of items on a table. Among such tasks, those characterised by high-speed manipulation requirements and general sensitivity of the resulting hybrid dynamics are particularly hard to accomplish. Within these, juggling can be seen as a highly challenging maneuver to be solved. The key to robotic juggling is achieving dynamic stabilisation of an underactuated object. Since the object does not possess the ability of self-correction, its stability is entirely dependent on the forces applied to it. This creates a system that is sensitive to control inputs, where timing is critical to continuously counteract deviations and maintain the desired behavior. We develop a systematic method to control a 7-degree-of-freedom manipulator performing non-prehensile ball juggling with a tool. Our primary contribution is a model-based framework for generating juggling trajectories and stabilizing a periodic juggling motion for this hybrid system. The framework incorporates a two-stage optimal control approach to compute the underlying feasible motion patterns required for stable juggling. Offline-computed trajectories are then organised to enable real-time error correction without solving optimal control problems online. We demonstrate the effectiveness of the resulting controller by first evaluating its performance in a simulation environment and performing an experiment using a Franka Emika Panda robot.

Constraint-Consistent Control of Task-Based and Kinematic RCM Constraints for Surgical Robots

Robotic-assisted minimally invasive surgery (RAMIS) requires precise enforcement of the remote center of motion (RCM) constraint to ensure safe tool manipulation through a trocar. Achieving this constraint under dynamic and interactive conditions remains challenging, as existing control methods either lack robustness at the torque level or do not guarantee consistent RCM constraint satisfaction. This paper proposes a constraint-consistent torque controller that treats the RCM as a rheonomic holonomic constraint and embeds it into a projection-based inverse-dynamics framework. The method unifies task-level and kinematic formulations, enabling accurate tool-tip tracking while maintaining smooth and efficient torque behavior. The controller is validated both in simulation and on a RAMIS training platform, and is benchmarked against state-of-the-art approaches. Results show improved RCM constraint satisfaction, reduced required torque, and robust performance by improving joint torque smoothness through the consistency formulation under clinically relevant scenarios, including spiral trajectories, variable insertion depths, moving trocars, and human interaction. These findings demonstrate the potential of constraint-consistent torque control to enhance safety and reliability in surgical robotics. The project page is available at: https://rcmpc-cube.github.io