Social Navigation

All Publication Topics Autonomous Driving Autonomous Exploration Benchmarks Compression Computer Vision First-Person Video Forecasting Generative Models Imitation Learning Intrinsic Control Machine Learning Manipulation Model-Based Control Offroad Navigation Paper Award Reinforcement Learning Reward Learning Robotics Social Navigation
Ratatouille: Imitation Learning Ingredients for Real-world Social Robot Navigation.
James R. Han, Mithun Vanniasinghe, Hshmat Sahak, Nicholas Rhinehart, Timothy D. Barfoot.
arXiv:2509.17204, (arXiv), 2025.
DOI PDF
Scaling Reinforcement Learning to in-the-wild social robot navigation is both data-intensive and unsafe, since policies must learn through direct interaction and inevitably encounter collisions. Offline Imitation learning (IL) avoids these risks by collecting expert demonstrations safely, training entirely offline, and deploying policies zero-shot. However, we find that naively applying Behaviour Cloning (BC) to social navigation is insufficient; achieving strong performance requires careful architectural and training choices. We present Ratatouille, a pipeline and model architecture that, without changing the data, reduces collisions per meter by 6 times and improves success rate by 3 times compared to naive BC. We validate our approach in both simulation and the real world, where we collected over 11 hours of data on a dense university campus. We further demonstrate qualitative results in a public food court. Our findings highlight that thoughtful IL design, rather than additional data, can substantially improve safety and reliability in real-world social navigation. Video: https://youtu.be/tOdLTXsaYLQ. Code will be released after acceptance.
@misc{han2025ratatouilleimitationlearningingredients,
      title={Ratatouille: Imitation Learning Ingredients for Real-world Social Robot Navigation}, 
      author={James R. Han and Mithun Vanniasinghe and Hshmat Sahak and Nicholas Rhinehart and Timothy D. Barfoot},
      year={2025},
      eprint={2509.17204},
      archivePrefix={arXiv},
      primaryClass={cs.RO},
      url={https://arxiv.org/abs/2509.17204}, 
}
Download
Machine Learning Robotics Imitation Learning Social Navigation
DR-MPC: Deep Residual Model Predictive Control for Real-world Social Navigation.
James R. Han, Hugues Thomas, Jian Zhang, Nicholas Rhinehart, Timothy D. Barfoot.
IEEE Robotics and Automation Letters, (RA-L), 2025.
DOI PDF Code
How can a robot safely navigate around people with complex motion patterns? Deep Reinforcement Learning (DRL) in simulation holds some promise, but much prior work relies on simulators that fail to capture the nuances of real human motion. Thus, we propose Deep Residual Model Predictive Control (DR-MPC) to enable robots to quickly and safely perform DRL from real-world crowd navigation data. By blending MPC with model-free DRL, DR-MPC overcomes the DRL challenges of large data requirements and unsafe initial behavior. DR-MPC is initialized with MPC-based path tracking, and gradually learns to interact more effectively with humans. To further accelerate learning, a safety component estimates out-of-distribution states to guide the robot away from likely collisions. In simulation, we show that DR-MPC substantially outperforms prior work, including traditional DRL and residual DRL models. Hardware experiments show our approach successfully enables a robot to navigate a variety of crowded situations with few errors using less than 4 hours of training data.
@article{han2024dr,
  title={DR-MPC: Deep Residual Model Predictive Control for Real-world Social Navigation},
  author={Han, James R and Thomas, Hugues and Zhang, Jian and Rhinehart, Nicholas and Barfoot, Timothy D},
  journal={IEEE Robotics and Automation Letters (RA-L)},
  year={2025}
}
Download
Computer Vision Forecasting Machine Learning Model-Based Control Robotics Reinforcement Learning Social Navigation