# World-Model-Based Control for Industrial box-packing of Multiple Objects using NewtonianVAE

arXiv: https://arxiv.org/abs/2308.02136 · HTML: https://mlsanity.com/paper/2308.02136/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Embodied and robot world models, one of 11 directions in World models.
- Builds on 16 of its 21 references that are in the map.
- First posted on arXiv 4 Aug 2023.

## Placed in

- World models / [Embodied and robot world models](https://mlsanity.com/wm/embodied-and-robot-world-models/)

## Builds on (most cited first, 16 of 16)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Dream to Control: Learning Behaviors by Latent Imagination](https://mlsanity.com/paper/1912.01603/) (2019)
- [Learning Latent Dynamics for Planning from Pixels](https://mlsanity.com/paper/1811.04551/) (2018)
- [Recurrent World Models Facilitate Policy Evolution](https://mlsanity.com/paper/1809.01999/) (2018)
- [Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Review](https://mlsanity.com/paper/1805.00909/) (2018)
- [Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images](https://mlsanity.com/paper/1506.07365/) (2015)
- [Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model](https://mlsanity.com/paper/1907.00953/) (2019)
- [Coarse-to-Fine Imitation Learning: Robot Manipulation from a Single Demonstration](https://mlsanity.com/paper/2105.06411/) (2021)
- [Dreaming: Model-based Reinforcement Learning by Latent Imagination without Reconstruction](https://mlsanity.com/paper/2007.14535/) (2020)
- [Learning Contact-Rich Manipulation Skills with Guided Policy Search](https://mlsanity.com/paper/1501.05611/) (2015)
- [DreamingV2: Reinforcement Learning with Discrete World Models without Reconstruction](https://mlsanity.com/paper/2203.00494/) (2022)
- [PlaNet of the Bayesians: Reconsidering and Improving Deep Planning Network by Incorporating Bayesian Inference](https://mlsanity.com/paper/2003.00370/) (2020)
- [NewtonianVAE: Proportional Control and Goal Identification from Pixels via Physical Latent Spaces](https://mlsanity.com/paper/2006.01959/) (2020)
- [Tactile-Based Insertion for Dense Box-Packing](https://mlsanity.com/paper/1909.05426/) (2019)
- [Domain-Adversarial and Conditional State Space Model for Imitation Learning](https://mlsanity.com/paper/2001.11628/) (2020)
- [Multi-View Dreaming: Multi-View World Model with Contrastive Learning](https://mlsanity.com/paper/2203.11024/) (2022)

## Abstract (arXiv)

The process of industrial box-packing, which involves the accurate placement of multiple objects, requires high-accuracy positioning and sequential actions. When a robot is tasked with placing an object at a specific location with high accuracy, it is important not only to have information about the location of the object to be placed, but also the posture of the object grasped by the robotic hand. Often, industrial box-packing requires the sequential placement of identically shaped objects into a single box. The robot's action should be determined by the same learned model. In factories, new kinds of products often appear and there is a need for a model that can easily adapt to them. Therefore, it should be easy to collect data to train the model. In this study, we designed a robotic system to automate real-world industrial tasks, employing a vision-based learning control model. We propose in-hand-view-sensitive Newtonian variational autoencoder (ihVS-NVAE), which employs an RGB camera to obtain in-hand postures of objects. We demonstrate that our model, trained for a single object-placement task, can handle sequential tasks without additional training. To evaluate efficacy of the proposed model, we employed a real robot to perform sequential industrial box-packing of multiple objects. Results showed that the proposed model achieved a 100% success rate in industrial box-packing tasks, thereby outperforming the state-of-the-art and conventional approaches, underscoring its superior effectiveness and potential in industrial tasks.