INTERACT: Intuitive Interaction for Robots among Humans

People

Saray Bakker

Andreu Matoses Gimenez

Dr. Clarence Chen

Prof. Javier Alonso-Mora - Autonomous Multi-Robot Lab (AMR) TU Delft

Prof. Wendelin Bohmer - Key collaborator.

Funding

This project is funded the ERC Starting Grant project "Intuitive Interaction for Humans among Robots (INTERACT)".

About the Project

INTERACT aims to equip mobile robots with the ability to navigate and operate safely in human-populated environments. Leveraging advancements in Motion Planning, Multi-robot Task Assignment, and Machine Learning, this project seeks to overcome the challenges of modeling intuition and ensuring safety in complex, uncertain settings. By developing intuitive models from past interactions and integrating them into novel optimization methods, INTERACT will enable robots to perform seamless, interaction-aware navigation and task planning. This foundational work paves the way for a new era of automation in both industrial and urban settings, where robots and humans can coexist harmoniously.

Ongoing Work

Funding & Partners

This project has received funding from the European Union through ERC, INTERACT, under Grant 101041863. Views and opinions expressed are, however, those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.

Related Publications

Physically Grounded Optimal Realizations of Symbolic Plans
Andreu Matoses Gimenez, Nils Wilde, Chris Pek, Javier Alonso-Mora. In Robotics: Science and Systems (RSS), 2024.

abstract pdf

Robot autonomy often involves planning high-level discrete decisions and continuous motion planning to realize each decision. Task and Motion Planning (TAMP) algorithms solve these hybrid problems jointly while considering constraints between the discrete symbolic actions, i.e., the task plan, and their continuous geometric realization. Previous TAMP algorithms have mostly focused on computational performance, completeness, or optimality. However, due to the required simplifications and abstractions, the resulting plans often do not account for robot dynamics, nor complex contacts. They also often ignore the effect of the low-level controllers on the optimality and/or feasibility of the plan's realizations. This work investigates the use of a parallelized physics simulator to compute realizations of the plan with a motion controller, realistic dynamics, and considering contacts with the environment. Using cross-entropy optimization, we sample the parameters used by the controllers, or actions, to obtain low-cost solutions. The resulting realized plan is straightforward to implement in the real system, as the robot uses the same controllers. We test our approach for a pick and place task, where our method is capable of finding low-cost feasible solutions in 1-2 min.

Decentralized Multi-Agent Trajectory Planning in Dynamic Environments with Spatiotemporal Occupancy Grid Maps
Siyuan Wu, Gang Chen, Moji Shi, Javier Alonso-Mora. In IEEE Int. Conf. on Robotics and Automation (ICRA), 2024.

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This paper proposes a decentralized trajectory planning framework for the collision avoidance problem of mul- tiple micro aerial vehicles (MAVs) in environments with static and dynamic obstacles. The framework utilizes spatiotemporal occupancy grid maps (SOGM), which forecast the occupancy status of neighboring space in the near future, as the environ- ment representation. Based on this representation, we extend the kinodynamic A* and the corridor-constrained trajectory optimization algorithms to efficiently tackle static and dynamic obstacles with arbitrary shapes. Collision avoidance between communicating robots is integrated by sharing planned tra- jectories and projecting them onto the SOGM. The simulation results show that our method achieves competitive performance against state-of-the-art methods in dynamic environments with different numbers and shapes of obstacles. Finally, the proposed method is validated in real experiments.

Multi-Robot Local Motion Planning Using Dynamic Optimization Fabrics
S. Bakker, L. Knoedler, M. Spahn, W. Böhmer, J. Alonso-Mora. In Proc. IEEE International Symposium on Multi-Robot and Multi-Agent Systems, 2023.

abstract pdf code video

Abstract: In this paper, we address the problem of real-time motion planning for multiple robotic manipulators that operate in close proximity. We build upon the concept of dynamic fabrics and extend them to multi-robot systems, referred to as Multi-Robot Dynamic Fabrics (MRDF). This geometric method enables a very high planning frequency for high-dimensional systems at the expense of being reactive and prone to deadlocks. To detect and resolve deadlocks, we propose Rollout Fabrics where MRDF are forward simulated in a decentralized manner. We validate the methods in simulated close-proximity pick-and-place scenarios with multiple manipulators, showing high success rates and real-time performance.