Robots must learn from experience and data to be efficient in unmodeled, unknown, and previously unseen domains. There are many methods for learning implicit models of the world, which capture everything from a 3D reconstruction of a scene, quantifying the risk of collision, understanding task constraints from human demonstrations, and more. There are endless opportunities for integrating these models within existing algorithm frameworks or new neurosymbolic approaches to generalize planning capabilities to previously considered intractable problems.
2024
Abstract
Perception-aware Planning for Robotics: Challenges and Opportunities
@inproceedings{meng2024icra40,author={Meng, Qingxi and Quintero-Peña, Carlos and Kingston, Zachary and Unhelkar, Vaibhav and Kavraki, Lydia E.},title={Perception-aware Planning for Robotics: Challenges and Opportunities},booktitle={ 40th Anniversary of the IEEE Conference on Robotics and Automation (ICRA@40)},year={2024},}
Motion planning under sensing uncertainty is critical for robots in unstructured environments to guarantee safety for both the robot and any nearby humans. Most work on planning under uncertainty does not scale to high-dimensional robots such as manipulators, assumes simplified geometry of the robot or environment, or requires per-object knowledge of noise. Instead, we propose a method that directly models sensor-specific aleatoric uncertainty to find safe motions for high-dimensional systems in complex environments, without exact knowledge of environment geometry. We combine a novel implicit neural model of stochastic signed distance functions with a hierarchical optimization-based motion planner to plan low-risk motions without sacrificing path quality. Our method also explicitly bounds the risk of the path, offering trustworthiness. We empirically validate that our method produces safe motions and accurate risk bounds and is safer than baseline approaches.
@inproceedings{quintero2024impdist,author={Quintero-Peña, Carlos and Thomason, Wil and Kingston, Zachary and Kyrillidis, Anastasios and Kavraki, Lydia E.},title={Stochastic Implicit Neural Signed Distance Functions for Safe Motion Planning under Sensing Uncertainty},year={2024},booktitle={IEEE International Conference on Robotics and Automation},pages={2360--2367},doi={10.1109/ICRA57147.2024.10610773},}
Workshop
Stochastic Implicit Neural Signed Distance Functions for Safe Motion Planning under Sensing Uncertainty
@inproceedings{Quintero2024wksp,author={Quintero-Peña, Carlos and Thomason, Wil and Kingston, Zachary and Kyrillidis, Anastasios and Kavraki, Lydia E.},title={Stochastic Implicit Neural Signed Distance Functions for Safe Motion Planning under Sensing Uncertainty},booktitle={IEEE ICRA 2024 Workshop---Back to the Future: Robot Learning Going Probabilistic},year={2024},url={https://arxiv.org/pdf/2309.16862.pdf},}
Workshop
Monitoring Constraints for Robotic Tutors in Nurse Education: A Motion Planning Perspective
@inproceedings{Meng2024wksp,author={Meng, Qingxi and Quintero-Peña, Carlos and Kingston, Zachary and Fontenot, Nicole M. and Hamlin, Shannan K. and Unhelkar, Vaibhav and Kavraki, Lydia E.},title={Monitoring Constraints for Robotic Tutors in Nurse Education: A Motion Planning Perspective},booktitle={IEEE ICRA 2024 Workshop---Workshop on Nursing Robotics},year={2024},}
3D object reconfiguration encompasses common robot manipulation tasks in which a set of objects must be moved through a series of physically feasible state changes into a desired final configuration. Object reconfiguration is challenging to solve in general, as it requires efficient reasoning about environment physics that determine action validity. This information is typically manually encoded in an explicit transition system. Constructing these explicit encodings is tedious and error-prone, and is often a bottleneck for planner use. In this work, we explore embedding a physics simulator within a motion planner to implicitly discover and specify the valid actions from any state, removing the need for manual specification of action semantics. Our experiments demonstrate that the resulting simulation-based planner can effectively produce physically valid rearrangement trajectories for a range of 3D object reconfiguration problems without requiring more than an environment description and start and goal arrangements.
@inproceedings{lee2023physics,title={Object Reconfiguration with Simulation-Derived Feasible Actions},author={Lee, Yiyuan and Thomason, Wil and Kingston, Zachary and Kavraki, Lydia E.},booktitle={IEEE International Conference on Robotics and Automation},year={2023},volume={},number={},pages={8104-8111},doi={10.1109/ICRA48891.2023.10160377},}
Earlier work has shown that reusing experience from prior motion planning problems can improve the efficiency of similar, future motion planning queries. However, for robots with many degrees-of-freedom, these methods exhibit poor generalization across different environments and often require large datasets that are impractical to gather. We present SPARK and FLAME, two experience-based frameworks for sampling- based planning applicable to complex manipulators in 3D environments. Both combine samplers associated with features from a workspace decomposition into a global biased sampling distribution. SPARK decomposes the environment based on exact geometry while FLAME is more general, and uses an octree-based decomposition obtained from sensor data. We demonstrate the effectiveness of SPARK and FLAME on a real and simulated Fetch robot tasked with challenging pick-and-place manipulation problems. Our approaches can be trained incrementally and significantly improve performance with only a handful of examples, generalizing better over diverse tasks and environments as compared to prior approaches.
@inproceedings{chamzas2021flame,author={Chamzas, Constantinos and Kingston, Zachary and Quintero-Peña, Carlos and Shrivastava, Anshumali and Kavraki, Lydia E.},title={Learning Sampling Distributions Using Local 3D Workspace Decompositions for Motion Planning in High Dimensions},booktitle={IEEE International Conference on Robotics and Automation},year={2021},doi={10.1109/ICRA48506.2021.9561104},pages={1283--1289},}
