Allowing Safe Contact in Robotic Goal-Reaching: Planning and Tracking in Operational and Null Spaces

TL;DR

This paper explores the benefits of allowing safe contact in robotic manipulation by planning in operational and null spaces, using a hybrid solver, to improve efficiency and safety in collision-prone environments.

Contribution

It introduces a novel planning approach that permits safe contact and combines sampling and gradient methods for trajectory optimization in robotic manipulation.

Findings

Allowing safe contact enhances goal-reaching efficiency.

Planning in null space improves trajectory safety.

The hybrid solver effectively optimizes collision-allowed trajectories.

Abstract

In recent years, impressive results have been achieved in robotic manipulation. While many efforts focus on generating collision-free reference signals, few allow safe contact between the robot bodies and the environment. However, in human's daily manipulation, contact between arms and obstacles is prevalent and even necessary. This paper investigates the benefit of allowing safe contact during robotic manipulation and advocates generating and tracking compliance reference signals in both operational and null spaces. In addition, to optimize the collision-allowed trajectories, we present a hybrid solver that integrates sampling- and gradient-based approaches. We evaluate the proposed method on a goal-reaching task in five simulated and real-world environments with different collisional conditions. We show that allowing safe contact improves goal-reaching efficiency and provides feasible solutions in highly collisional scenarios where collision-free constraints cannot be enforced. Moreover, we demonstrate that planning in null space, in addition to operational space, improves trajectory safety.