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Crowd Navigation using Reinforcement Learning
This project aims to address the challenges faced by planners in dynamic environments with fast-moving crowds. It proposes a new planner utilizing 3D sensing and Reinforcement Learning to understand human movement patterns, with the core objective of developing a reliable 3D sensor encoding integrated into a novel planner architecture within the Orbit Framework, specifically designed for crowded environments.
Keywords: Reinforcement Learning, 3D Sensors, LiDAR, Planning, Navigation, Isaac Sim, Orbit
This project seeks to overcome the limitations faced by planners in dynamic environments, particularly those with fast-moving crowds. While recent advancements in end-to-end trained local planners have demonstrated success in demanding scenarios, their primary focus is on static scenes. However, the need for high update frequencies in dynamic environments remains a significant challenge [3,4]. To address these issues, we propose developing a new planner using 3D sensing that is able to reason about human movement patterns. For training, we aim to leverage the latest developments in Reinforcement Learning (RL) [1,2], known for its efficacy in locomotion tasks. In such an approach, the planner will learn from experience how humans move and the best way to plan a path around them. The core objective of this project is the development of the 3D sensor encoding that is fed into a novel planner architecture, all trained within the Orbit Framework [5] and specifically designed for reliability in crowded environments.
References:
[1] Nikita Rudin, David Hoeller, Philipp Reist, Marco Hutter, Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning, Proceedings of the 5th Conference on Robot Learning, PMLR 164:91-100, 2022.
[2] Miki, T., Lee, J., Hwangbo, J., Wellhausen, L., Koltun, V. and Hutter, M., 2022. Learning robust perceptive locomotion for quadrupedal robots in the wild. Science Robotics, 7
[3] Roth, P., Nubert, J., Yang, F., Mittal, M. and Hutter, M., 2023. ViPlanner: Visual Semantic Imperative Learning for Local Navigation. arXiv preprint arXiv:2310.00982.
[4] Yang, F., Wang, C., Cadena, C. and Hutter, M., 2023. iPlanner: Imperative Path Planning. arXiv preprint arXiv:2302.11434.
[5] Mittal, M., Yu, C., Yu, Q., Liu, J., Rudin, N., Hoeller, D., Yuan, J.L., Singh, R., Guo, Y., Mazhar, H. and Mandlekar, A., 2023. Orbit: A unified simulation framework for interactive robot learning environments. IEEE Robotics and Automation Letters.
This project seeks to overcome the limitations faced by planners in dynamic environments, particularly those with fast-moving crowds. While recent advancements in end-to-end trained local planners have demonstrated success in demanding scenarios, their primary focus is on static scenes. However, the need for high update frequencies in dynamic environments remains a significant challenge [3,4]. To address these issues, we propose developing a new planner using 3D sensing that is able to reason about human movement patterns. For training, we aim to leverage the latest developments in Reinforcement Learning (RL) [1,2], known for its efficacy in locomotion tasks. In such an approach, the planner will learn from experience how humans move and the best way to plan a path around them. The core objective of this project is the development of the 3D sensor encoding that is fed into a novel planner architecture, all trained within the Orbit Framework [5] and specifically designed for reliability in crowded environments.
References:
[1] Nikita Rudin, David Hoeller, Philipp Reist, Marco Hutter, Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning, Proceedings of the 5th Conference on Robot Learning, PMLR 164:91-100, 2022.
[2] Miki, T., Lee, J., Hwangbo, J., Wellhausen, L., Koltun, V. and Hutter, M., 2022. Learning robust perceptive locomotion for quadrupedal robots in the wild. Science Robotics, 7
[3] Roth, P., Nubert, J., Yang, F., Mittal, M. and Hutter, M., 2023. ViPlanner: Visual Semantic Imperative Learning for Local Navigation. arXiv preprint arXiv:2310.00982.
[4] Yang, F., Wang, C., Cadena, C. and Hutter, M., 2023. iPlanner: Imperative Path Planning. arXiv preprint arXiv:2302.11434.
[5] Mittal, M., Yu, C., Yu, Q., Liu, J., Rudin, N., Hoeller, D., Yuan, J.L., Singh, R., Guo, Y., Mazhar, H. and Mandlekar, A., 2023. Orbit: A unified simulation framework for interactive robot learning environments. IEEE Robotics and Automation Letters.
Literature review on local planning algorithms and training strategies
Implementation of a novel planner with 3D Sensor encoding
Integration with ANYmal planning and navigation framework for fast local planning
Real-world evaluation of trained planners
Literature review on local planning algorithms and training strategies
Implementation of a novel planner with 3D Sensor encoding
Integration with ANYmal planning and navigation framework for fast local planning
Real-world evaluation of trained planners
Highly motivated for the topic
Programming experience (C++/Python)
Experience with deep learning projects (preferably with RL)
Knowledge of planning, perception, and robot dynamics is a plus
Highly motivated for the topic
Programming experience (C++/Python)
Experience with deep learning projects (preferably with RL)
Knowledge of planning, perception, and robot dynamics is a plus
Please send your CV, TOR and a short motivational statement to Fan Yang (fanyang1@ethz.ch), Takahiro Miki (tamiki@ethz.ch) and Pascal Roth (rothpa@ethz.ch)
Please send your CV, TOR and a short motivational statement to Fan Yang (fanyang1@ethz.ch), Takahiro Miki (tamiki@ethz.ch) and Pascal Roth (rothpa@ethz.ch)