# Environment-aware Interactive Movement Primitives for Object Reaching in Clutter

arXiv: https://arxiv.org/abs/2210.16194 · HTML: https://mlsanity.com/paper/2210.16194/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 15 references that are in the map.
- First posted on arXiv 28 Oct 2022.

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

- [Motion Planning Networks](https://mlsanity.com/paper/1806.05767/) (2018)
- [HRL4IN: Hierarchical Reinforcement Learning for Interactive Navigation with Mobile Manipulators](https://mlsanity.com/paper/1910.11432/) (2019)
- [Learning a State Representation and Navigation in Cluttered and Dynamic Environments](https://mlsanity.com/paper/2103.04351/) (2021)
- [Reinforcement Learning of Active Vision for Manipulating Objects under Occlusions](https://mlsanity.com/paper/1811.08067/) (2018)
- [Planning with a Receding Horizon for Manipulation in Clutter using a Learned Value Function](https://mlsanity.com/paper/1803.08100/) (2018)

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

- [Learning Manipulation Tasks in Dynamic and Shared 3D Spaces](https://mlsanity.com/paper/2404.17673/) (2024)

## Abstract (arXiv)

The majority of motion planning strategies developed over the literature for reaching an object in clutter are applied to two dimensional (2-d) space where the state space of the environment is constrained in one direction. Fewer works have been investigated to reach a target in 3-d cluttered space, and when so, they have limited performance when applied to complex cases. In this work, we propose a constrained multi-objective optimization framework (OptI-ProMP) to approach the problem of reaching a target in a compact clutter with a case study on soft fruits grown in clusters, leveraging the local optimisation-based planner CHOMP. OptI-ProMP features costs related to both static, dynamic and pushable objects in the target neighborhood, and it relies on probabilistic primitives for problem initialisation. We tested, in a simulated poly-tunnel, both ProMP-based planners from literature and the OptI-ProMP, on low (3-dofs) and high (7-dofs) dexterity robot body, respectively. Results show collision and pushing costs minimisation with 7-dofs robot kinematics, in addition to successful static obstacles avoidance and systematic drifting from the pushable objects center of mass.