# Learning to Represent Surroundings, Anticipate Motion and Take Informed Actions in Unstructured Environments

arXiv: https://arxiv.org/abs/2407.10383 · HTML: https://mlsanity.com/paper/2407.10383/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 197 references that are in the map.
- First posted on arXiv 15 Jul 2024.

## Builds on (most cited first, 11 of 11)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Implicit Behavioral Cloning](https://mlsanity.com/paper/2109.00137/) (2021)
- [Reducing the Barrier to Entry of Complex Robotic Software: a MoveIt! Case Study](https://mlsanity.com/paper/1404.3785/) (2014)
- [Riemannian Motion Policies](https://mlsanity.com/paper/1801.02854/) (2018)
- [Euclideanizing Flows: Diffeomorphic Reduction for Learning Stable Dynamical Systems](https://mlsanity.com/paper/2005.13143/) (2020)
- [Learning Contracting Vector Fields For Stable Imitation Learning](https://mlsanity.com/paper/1804.04878/) (2018)
- [Parallelised Diffeomorphic Sampling-based Motion Planning](https://mlsanity.com/paper/2108.11775/) (2021)
- [TensorFlow: A system for large-scale machine learning](https://mlsanity.com/paper/1605.08695/) (2016)
- [Occ-Traj120: Occupancy Maps with Associated Trajectories](https://mlsanity.com/paper/1909.02333/) (2019)
- [OCTNet: Trajectory Generation in New Environments from Past Experiences](https://mlsanity.com/paper/1909.11337/) (2019)

## Abstract (arXiv)

Contemporary robots have become exceptionally skilled at achieving specific tasks in structured environments. However, they often fail when faced with the limitless permutations of real-world unstructured environments. This motivates robotics methods which learn from experience, rather than follow a pre-defined set of rules. In this thesis, we present a range of learning-based methods aimed at enabling robots, operating in dynamic and unstructured environments, to better understand their surroundings, anticipate the actions of others, and take informed actions accordingly.