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

TL;DR

This paper introduces ToMPC, a task-oriented model predictive control framework that uses ADMM to enable safe, efficient, and real-time robotic manipulation with obstacle avoidance and constraint adherence.

Contribution

It presents a novel unified optimization framework combining collision avoidance and task-specific control using ADMM, DDP, and QP for robotic manipulation.

Findings

Real-time motion and force trajectory planning demonstrated in simulation and hardware.

Enhanced manipulation range with obstacle avoidance.

Strict adherence to safety constraints in complex scenarios.

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.