# Learning Interaction-Aware Trajectory Predictions for Decentralized Multi-Robot Motion Planning in Dynamic Environments

arXiv: https://arxiv.org/abs/2102.05382 · HTML: https://mlsanity.com/paper/2102.05382/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 39 references that are in the map.
- First posted on arXiv 10 Feb 2021.

## Builds on (most cited first, 14 of 14)

- [Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks](https://mlsanity.com/paper/1803.10892/) (2018)
- [DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agents](https://mlsanity.com/paper/1704.04394/) (2017)
- [Human Motion Trajectory Prediction: A Survey](https://mlsanity.com/paper/1905.06113/) (2019)
- [Decentralized Non-communicating Multiagent Collision Avoidance with Deep Reinforcement Learning](https://mlsanity.com/paper/1609.07845/) (2016)
- [What the Constant Velocity Model Can Teach Us About Pedestrian Motion Prediction](https://mlsanity.com/paper/1903.07933/) (2019)
- [Multimodal Probabilistic Model-Based Planning for Human-Robot Interaction](https://mlsanity.com/paper/1710.09483/) (2017)
- [A Data-driven Model for Interaction-aware Pedestrian Motion Prediction in Object Cluttered Environments](https://mlsanity.com/paper/1709.08528/) (2017)
- [Multi-agent Motion Planning for Dense and Dynamic Environments via Deep Reinforcement Learning](https://mlsanity.com/paper/2001.06627/) (2020)
- [GLAS: Global-to-Local Safe Autonomy Synthesis for Multi-Robot Motion Planning with End-to-End Learning](https://mlsanity.com/paper/2002.11807/) (2020)
- [Neural-Swarm: Decentralized Close-Proximity Multirotor Control Using Learned Interactions](https://mlsanity.com/paper/2003.02992/) (2020)
- [Online Trajectory Generation with Distributed Model Predictive Control for Multi-Robot Motion Planning](https://mlsanity.com/paper/1909.05150/) (2019)
- [Social-VRNN: One-Shot Multi-modal Trajectory Prediction for Interacting Pedestrians](https://mlsanity.com/paper/2010.09056/) (2020)
- [I-Planner: Intention-Aware Motion Planning Using Learning Based Human Motion Prediction](https://mlsanity.com/paper/1608.04837/) (2016)
- [With Whom to Communicate: Learning Efficient Communication for Multi-Robot Collision Avoidance](https://mlsanity.com/paper/2009.12106/) (2020)

## Built on by (most cited first, 12 of 12)

- [Socially Compliant Navigation Dataset (SCAND): A Large-Scale Dataset of Demonstrations for Social Navigation](https://mlsanity.com/paper/2203.15041/) (2022)
- [MART: MultiscAle Relational Transformer Networks for Multi-agent Trajectory Prediction](https://mlsanity.com/paper/2407.21635/) (2024)
- [Targeted Adversarial Attacks against Neural Network Trajectory Predictors](https://mlsanity.com/paper/2212.04138/) (2022)
- [Time-series Imputation of Temporally-occluded Multiagent Trajectories](https://mlsanity.com/paper/2106.04219/) (2021)
- [Multi-Condition Latent Diffusion Network for Scene-Aware Neural Human Motion Prediction](https://mlsanity.com/paper/2405.18700/) (2024)
- [Residual Neural Terminal Constraint for MPC-based Collision Avoidance in Dynamic Environments](https://mlsanity.com/paper/2508.03428/) (2025)
- [Simulation-Informed Diffusion for Decentralized Multi-robot Motion Planning](https://mlsanity.com/paper/2605.27697/) (2026)
- [SAFER: Safe Collision Avoidance using Focused and Efficient Trajectory Search with Reinforcement Learning](https://mlsanity.com/paper/2209.11789/) (2022)
- [ARMCHAIR: integrated inverse reinforcement learning and model predictive control for human-robot collaboration](https://mlsanity.com/paper/2402.19128/) (2024)
- [Adaptive Anomaly Recovery for Telemanipulation: A Diffusion Model Approach to Vision-Based Tracking](https://mlsanity.com/paper/2503.09632/) (2025)
- [PatchTraj: Unified Time-Frequency Representation Learning via Dynamic Patches for Trajectory Prediction](https://mlsanity.com/paper/2507.19119/) (2025)
- [LEARN: Learning End-to-End Aerial Resource-Constrained Multi-Robot Navigation](https://mlsanity.com/paper/2511.17765/) (2025)

## Abstract (arXiv)

This paper presents a data-driven decentralized trajectory optimization approach for multi-robot motion planning in dynamic environments. When navigating in a shared space, each robot needs accurate motion predictions of neighboring robots to achieve predictive collision avoidance. These motion predictions can be obtained among robots by sharing their future planned trajectories with each other via communication. However, such communication may not be available nor reliable in practice. In this paper, we introduce a novel trajectory prediction model based on recurrent neural networks (RNN) that can learn multi-robot motion behaviors from demonstrated trajectories generated using a centralized sequential planner. The learned model can run efficiently online for each robot and provide interaction-aware trajectory predictions of its neighbors based on observations of their history states. We then incorporate the trajectory prediction model into a decentralized model predictive control (MPC) framework for multi-robot collision avoidance. Simulation results show that our decentralized approach can achieve a comparable level of performance to a centralized planner while being communication-free and scalable to a large number of robots. We also validate our approach with a team of quadrotors in real-world experiments.