# Planning Robot Placement for Object Grasping

arXiv: https://arxiv.org/abs/2405.16692 · HTML: https://mlsanity.com/paper/2405.16692/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 22 references that are in the map.
- First posted on arXiv 26 May 2024.

## Builds on (most cited first, 5 of 5)

- [Open3D: A Modern Library for 3D Data Processing](https://mlsanity.com/paper/1801.09847/) (2018)
- [Data-Driven Grasp Synthesis - A Survey](https://mlsanity.com/paper/1309.2660/) (2013)
- [Robot Learning of Mobile Manipulation with Reachability Behavior Priors](https://mlsanity.com/paper/2203.04051/) (2022)
- [Constrained Probabilistic Movement Primitives for Robot Trajectory Adaptation](https://mlsanity.com/paper/2101.12561/) (2021)
- [Learning Sensor Feedback Models from Demonstrations via Phase-Modulated Neural Networks](https://mlsanity.com/paper/1710.08555/) (2017)

## Built on by (most cited first, 1 of 1)

- [GBPP: Grasp-Aware Base Placement Prediction for Robots via Two-Stage Learning](https://mlsanity.com/paper/2509.11594/) (2025)

## Abstract (arXiv)

When performing manipulation-based activities such as picking objects, a mobile robot needs to position its base at a location that supports successful execution. To address this problem, prominent approaches typically rely on costly grasp planners to provide grasp poses for a target object, which are then are then analysed to identify the best robot placements for achieving each grasp pose. In this paper, we propose instead to first find robot placements that would not result in collision with the environment and from where picking up the object is feasible, then evaluate them to find the best placement candidate. Our approach takes into account the robot's reachability, as well as RGB-D images and occupancy grid maps of the environment for identifying suitable robot poses. The proposed algorithm is embedded in a service robotic workflow, in which a person points to select the target object for grasping. We evaluate our approach with a series of grasping experiments, against an existing baseline implementation that sends the robot to a fixed navigation goal. The experimental results show how the approach allows the robot to grasp the target object from locations that are very challenging to the baseline implementation.