# Goal-directed Planning and Goal Understanding by Active Inference: Evaluation Through Simulated and Physical Robot Experiments

arXiv: https://arxiv.org/abs/2202.09976 · HTML: https://mlsanity.com/paper/2202.09976/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 38 references that are in the map.
- First posted on arXiv 21 Feb 2022.

## Builds on (most cited first, 12 of 12)

- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Dream to Control: Learning Behaviors by Latent Imagination](https://mlsanity.com/paper/1912.01603/) (2019)
- [Hindsight Experience Replay](https://mlsanity.com/paper/1707.01495/) (2017)
- [Planning with Goal-Conditioned Policies](https://mlsanity.com/paper/1911.08453/) (2019)
- [Discovering and Achieving Goals via World Models](https://mlsanity.com/paper/2110.09514/) (2021)
- [Unsupervised Control Through Non-Parametric Discriminative Rewards](https://mlsanity.com/paper/1811.11359/) (2018)
- [Deep active inference agents using Monte-Carlo methods](https://mlsanity.com/paper/2006.04176/) (2020)
- [Exploration and preference satisfaction trade-off in reward-free learning](https://mlsanity.com/paper/2106.04316/) (2021)
- [Learning, Planning, and Control in a Monolithic Neural Event Inference Architecture](https://mlsanity.com/paper/1809.07412/) (2018)
- [An active inference implementation of phototaxis](https://mlsanity.com/paper/1707.01806/) (2017)
- [Adaptation through prediction: multisensory active inference torque control](https://mlsanity.com/paper/2112.06752/) (2021)
- [Goal-Directed Planning for Habituated Agents by Active Inference Using a Variational Recurrent Neural Network](https://mlsanity.com/paper/2005.14656/) (2020)

## Built on by (most cited first, 4 of 4)

- [On efficient computation in active inference](https://mlsanity.com/paper/2307.00504/) (2023)
- [Habits and goals in synergy: a variational Bayesian framework for behavior](https://mlsanity.com/paper/2304.05008/) (2023)
- [Emergence of Goal-Directed Behaviors via Active Inference with Self-Prior](https://mlsanity.com/paper/2504.11075/) (2025)
- [CogNarr Ecosystem: Facilitating Group Cognition at Scale](https://mlsanity.com/paper/2407.18945/) (2024)

## Abstract (arXiv)

We show that goal-directed action planning and generation in a teleological framework can be formulated using the free energy principle. The proposed model, which is built on a variational recurrent neural network model, is characterized by three essential features. These are that (1) goals can be specified for both static sensory states, e.g., for goal images to be reached and dynamic processes, e.g., for moving around an object, (2) the model can not only generate goal-directed action plans, but can also understand goals by sensory observation, and (3) the model generates future action plans for given goals based on the best estimate of the current state, inferred using past sensory observations. The proposed model is evaluated by conducting experiments on a simulated mobile agent as well as on a real humanoid robot performing object manipulation.