Constantinos Chamzas

selected publications

1. ICRA

   Learning to Retrieve Relevant Experiences for Motion Planning

   C. Chamzas, A. Cullen, A. Shrivastava, and L. E. Kavraki

   In IEEE International Conference on Robotics and Automation, 2022

   Abs Bib PDF URL TALK

   Recent work has demonstrated that motion planners’ performance can be significantly improved by retrieving past experiences from a database. Typically, the experience database is queried for past similar problems using a similarity function defined over the motion planning problems. However, to date, most works rely on simple hand-crafted similarity functions and fail to generalize outside their corresponding training dataset. To address this limitation, we propose (FIRE), aframework that extracts local representations of planning problems and learns a similarity function over them. To generate the training data we introduce a novel self-supervised method that identifies similar and dissimilar pairs of local primitives from past solution paths. With these pairs, a Siamese network is trained with the contrastive loss and the similarity function is realized in the network’s latent space. We evaluate FIRE on an 8-DOF manipulator in five categories of motion planning problems with sensed environments. Our experiments show that FIRE retrieves relevant experiences which can informatively guide sampling-based planners even in problems outside its training distribution, outperforming other baselines.

   @inproceedings{chamzas2022-learn-retrieve,
     author = {Chamzas, Constantinos and Cullen, Aedan and Shrivastava, Anshumali and E. Kavraki, Lydia},
     booktitle = {IEEE International Conference on Robotics and Automation},
     month = may,
     pages = {7233--7240},
     doi = {10.1109/ICRA46639.2022.9812076},
     title = {Learning to Retrieve Relevant Experiences for Motion Planning},
     url = {https://doi.org/10.1109/ICRA46639.2022.9812076},
     year = {2022}
   }

2. RAL

   MotionBenchMaker: A Tool to Generate and Benchmark Motion Planning Datasets

   C. Chamzas, C. Quintero-Peña, Z. Kingston, A. Orthey, D. Rakita, M. Gleicher, M. Toussaint, and L. E. Kavraki

   IEEE Robotics and Automation Letters, 2022

   Abs Bib PDF Code URL video TALK

   Recently, there has been a wealth of development in motion planning for robotic manipulationnew motion planners are continuously proposed, each with its own unique set of strengths and weaknesses. However, evaluating these new planners is challenging, and researchers often create their own ad-hoc problems for benchmarking, which is time-consuming, prone to bias, and does not directly compare against other state-of-the-art planners. We present MotionBenchMaker, an open-source tool to generate benchmarking datasets for realistic robot manipulation problems. MotionBenchMaker is designed to be an extensible, easy-to-use tool that allows users to both generate datasets and benchmark them by comparing motion planning algorithms. Empirically, we show the benefit of using MotionBenchMaker as a tool to procedurally generate datasets which helps in the fair evaluation of planners. We also present a suite of over 40 prefabricated datasets, with 5 different commonly used robots in 8 environments, to serve as a common ground for future motion planning research.

   @article{chamzas2022-motion-bench-maker,
     title = {MotionBenchMaker: A Tool to Generate and Benchmark Motion Planning Datasets},
     volume = {7},
     number = {2},
     pages = {882–889},
     issn = {2377-3766},
     doi = {10.1109/LRA.2021.3133603},
     journal = {IEEE Robotics and Automation Letters},
     author = {Chamzas, Constantinos and Quintero-Pe{\~n}a, Carlos and Kingston, Zachary and Orthey, Andreas and Rakita, Daniel and Gleicher, Michael and Toussaint, Marc and E. Kavraki, Lydia},
     year = {2022},
     month = apr,
     url = {https://dx.doi.org/10.1109/LRA.2021.3133603}
   }

3. ICRA

   Learning Sampling Distributions Using Local 3D Workspace Decompositions for Motion Planning in High Dimensions

   C. Chamzas, Z. Kingston, C. Quintero-Peña, A. Shrivastava, and L. E. Kavraki

   In IEEE International Conference on Robotics and Automation, 2021
   (Top-4 finalist for best paper in Cognitive Robotics)
   

   Abs Bib PDF Code URL video TALK

   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 3 D 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{chamzas2021-learn-sampling,
     author = {Chamzas, Constantinos and Kingston, Zachary and Quintero-Pe{\~n}a, Carlos and Shrivastava, Anshumali and E. Kavraki, Lydia},
     booktitle = {IEEE International Conference on Robotics and Automation},
     month = jun,
     title = {{Learning Sampling Distributions Using Local 3D Workspace Decompositions for Motion
           Planning in High Dimensions}},
     year = {2021},
     url = {https://dx.doi.org/10.1109/ICRA48506.2021.9561104},
     doi = {10.1109/ICRA48506.2021.9561104},
     pages = {1283-1289}
   }