Publications

@inproceedings{safe_reconstruction,
title = {Safe, Occlusion-Aware Manipulation for Online Object Reconstruction in Confined Space},
author = {Y Miao and R Wang and K E Bekris},
url = {https://arxiv.org/abs/2205.11719},
year = {2022},
date = {2022-09-25},
booktitle = {International Symposium on Robotics Research (ISRR) },
abstract = {Recent work in robotic manipulation focuses on object retrieval in cluttered space under occlusion. Nevertheless, the majority of efforts lack an analysis of conditions for the completeness of the approaches or the methods apply only when objects can be removed from the workspace. This work formulates the general, occlusion-aware manipulation task, and focuses on safe object reconstruction in a confined space with in-place relocation. A framework that ensures safety with completeness guarantees is proposed. Furthermore, an algorithm, which is an instantiation of this framework for monotone instances, is developed and evaluated empirically by comparing against a random and a greedy baseline on randomly generated experiments in simulation. Even for cluttered scenes with realistic objects, the proposed algorithm significantly outperforms the baselines and maintains a high success rate across experimental conditions. },
keywords = {Manipulation, Planning, Robot Perception},
pubstate = {published},
tppubtype = {inproceedings}
}

@article{survey_learning_planning,
title = {A Survey on the Integration of Machine Learning with Sampling-based Motion Planning},
author = {T McMahon and A Sivaramakrishnan and E Granados and K E Bekris},
year = {2022},
date = {2022-06-20},
journal = {Foundations and Trends in Robotics},
keywords = {Learning, Planning},
pubstate = {forthcoming},
tppubtype = {article}
}

@inproceedings{terrain_sampling_simulated,
title = {Terrain-Aware Learned Controllers for Sampling-Based Kinodynamic Planning over Physically Simulated Terrains},
author = {T McMahon and A Sivaramakrishnan and K Kedia and E Granados and K E Bekris },
year = {2022},
date = {2022-06-01},
booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
keywords = {Dynamics, Learning, Planning},
pubstate = {published},
tppubtype = {inproceedings}
}

@article{204,
title = {dRRT*: Scalable and Informed Asymptotically-Optimal Multi-Robot Motion Planning},
author = {R Shome and K Solovey and A Dobson and D Halperin and K E Bekris},
url = {https://www.cs.rutgers.edu/~kb572/pubs/drrt_star_auro.pdf},
year = {2020},
date = {2020-01-24},
journal = {Autonomous Robots},
abstract = {Many exciting robotic applications require multiple robots with many degrees of freedom, such as manipulators, to coordinate their motion in a shared workspace. Discovering high-quality paths in such scenarios can be achieved, in principle, by exploring the composite space of all robots. Sampling-based planners do so by building a roadmap or a tree data structure in the corresponding configuration space and can achieve asymptotic optimality. The hardness of motion planning, however, renders the explicit construction of such structures in the composite space of multiple robots impractical. This work proposes a scalable solution for such coupled multi-robot problems, which provides desirable path-quality guarantees and is also computationally efficient. In particular, the proposed dRRT* is an informed, asymptotically-optimal extension of a prior sampling-based multi-robot motion planner, dRRT. The prior approach introduced the idea of building roadmaps for each robot and implicitly searching the tensor product of these structures in the composite space. This work identifies the conditions for convergence to optimal paths in multi-robot problems, which the prior method was not achieving.},
keywords = {Multi-Robot, Planning},
pubstate = {published},
tppubtype = {article}
}