ToMPC: Task-oriented Model Predictive Control via ADMM for Safe Robotic Manipulation

Abstract

This paper proposes a task-oriented model predictive control (ToMPC) framework for safe and efficient robotic manipulation in open workspaces. The framework unifies collision-free motion and robot-environment interaction to address diverse scenarios. Additionally, it introduces task-oriented obstacle avoidance that leverages kinematic redundancy to enhance manipulation efficiency in obstructed environments. This complex optimization problem is solved by the alternating direction method of multipliers (ADMM), which decomposes the problem into two subproblems tackled by differential dynamic programming (DDP) and quadratic programming (QP), respectively. The effectiveness of this approach is validated in simulation and hardware experiments on a Franka Panda robotic manipulator. Results demonstrate that the framework can plan motion and/or force trajectories in real time, maximize the manipulation range while avoiding obstacles, and strictly adhere to safety-related hard constraints.

Figures (10)

• Figure 1: A schematic diagram depicting the the proposed ToMPC planner, where the ADMM solves the optimization problem in a distributed fashion.
• Figure 2: Illustration of interaction model (left) and task-oriented avoidance (right).
• Figure 3: Scenario $\#$1: Simulation of the robot performing obstacle avoidance with various shapes and quantities of surrounding objects.
• Figure 4: Simulation results for Scenario #1. Top: Minimum distances to the obstacle (Fig. \ref{['fig:1_sim']}Left). The FDDP and ADMM methods are compared under different parameter settings. Bottom: Cartesian trajectories for all six test cases.
• Figure 5: Scenario #2: The robot manipulates a red block while avoiding a ball obstacle. The two diagrams are experimental snapshots of conventional avoidance (oaMPC) and task-oriented avoidance (ToMPC), respectively.