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

arXiv: https://arxiv.org/abs/2211.08199 · HTML: https://mlsanity.com/paper/2211.08199/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2022.

## Built on by (most cited first, 3 of 3)

- [Multi-level Reasoning for Robotic Assembly: From Sequence Inference to Contact Selection](https://mlsanity.com/paper/2312.10571/) (2023)
- [Diff-Transfer: Model-based Robotic Manipulation Skill Transfer via Differentiable Physics Simulation](https://mlsanity.com/paper/2310.04930/) (2023)
- [Enhanced Robotic Navigation in Deformable Environments using Learning from Demonstration and Dynamic Modulation](https://mlsanity.com/paper/2506.20376/) (2025)

## Abstract (arXiv)

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.