# Implicit Contact-Rich Manipulation Planning for a Manipulator with Insufficient Payload

## TL;DR

This paper introduces a probabilistic planning method enabling a mobile manipulator to manipulate objects beyond its payload capacity by leveraging environmental support without explicit contact mode analysis.

## Contribution

It presents a novel single-shot probabilistic roadmap approach that automatically plans contact-rich manipulation motions considering environment support and force constraints.

## Key findings

- Successfully manipulates heavy objects using environmental support.
- Avoids explicit contact mode analysis and transition graphs.
- Validated through simulations and real-world experiments.

## Abstract

This paper uses a mobile manipulator with a collaborative robotic arm to manipulate objects beyond the robot's maximum payload. It proposes a single-shot probabilistic roadmap-based method to plan and optimize manipulation motion with environment support. The method uses an expanded object mesh model to examine contact and randomly explores object motion while keeping contact and securing affordable grasping force. It generates robotic motion trajectories after obtaining object motion using an optimization-based algorithm. With the proposed method's help, we can plan contact-rich manipulation without particularly analyzing an object's contact modes and their transitions. The planner and optimizer determine them automatically. We conducted experiments and analyses using simulations and real-world executions to examine the method's performance. It can successfully find manipulation motion that met contact, force, and kinematic constraints, thus allowing a mobile manipulator to move heavy objects while leveraging supporting forces from environmental obstacles. The mehtod does not need to explicitly analyze contact states and build contact transition graphs, thus providing a new view for robotic grasp-less manipulation, non-prehensile manipulation, manipulation with contact, etc.