# dPMP-Deep Probabilistic Motion Planning: A use case in Strawberry Picking Robot

arXiv: https://arxiv.org/abs/2208.09074 · HTML: https://mlsanity.com/paper/2208.09074/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Robot learning from demonstration, one of 13 directions in Offline RL and imitation learning from data.
- First posted on arXiv 18 Aug 2022.

## Placed in

- Offline RL and imitation learning from data / [Robot learning from demonstration](https://mlsanity.com/offlinerl/robot-learning-from-demonstration/)

## Built on by (most cited first, 5 of 5)

- [Push Past Green: Learning to Look Behind Plant Foliage by Moving It](https://mlsanity.com/paper/2307.03175/) (2023)
- [Autonomous Strawberry Picking Robotic System (Robofruit)](https://mlsanity.com/paper/2301.03947/) (2023)
- [Find the Fruit: Zero-Shot Sim2Real RL for Occlusion-Aware Plant Manipulation](https://mlsanity.com/paper/2505.16547/) (2025)
- [Multi-Modal World Model for Physical Robot Interactions: Simultaneous Visual and Tactile Predictions for Enhanced Accuracy](https://mlsanity.com/paper/2304.11193/) (2023)
- [DualLQR: Efficient Grasping of Oscillating Apples using Task Parameterized Learning from Demonstration](https://mlsanity.com/paper/2409.16957/) (2024)

## Abstract (arXiv)

This paper presents a novel probabilistic approach to deep robot learning from demonstrations (LfD). Deep movement primitives (DMPs) are deterministic LfD model that maps visual information directly into a robot trajectory. This paper extends DMPs and presents a deep probabilistic model that maps the visual information into a distribution of effective robot trajectories. The architecture that leads to the highest level of trajectory accuracy is presented and compared with the existing methods. Moreover, this paper introduces a novel training method for learning domain-specific latent features. We show the superiority of the proposed probabilistic approach and novel latent space learning in the lab's real-robot task of strawberry harvesting. The experimental results demonstrate that latent space learning can significantly improve model prediction performances. The proposed approach allows to sample trajectories from distribution and optimises the robot trajectory to meet a secondary objective, e.g. collision avoidance.