Publications

@inproceedings{tensegrity_pose_tracking,
title = {6N-DoF Pose Tracking for Tensegrity Robots},
author = {S Lu and W Johnson and K Wang and X Huang and J Booth and R Kramer-Bottiglio and K E Bekris},
url = {https://arxiv.org/abs/2205.14764},
year = {2022},
date = {2022-09-26},
booktitle = {International Symposium on Robotics Research (ISRR)},
abstract = {Tensegrity robots, which are composed of rigid compressive elements (rods) and flexible tensile elements (e.g., cables), have a variety of advantages, including flexibility, light weight, and resistance to mechanical impact. Nevertheless, the hybrid soft-rigid nature of these robots also complicates the ability to localize and track their state. This work aims to address what has been recognized as a grand challenge in this domain, i.e., the pose tracking of tensegrity robots through a markerless, vision-based method, as well as novel, onboard sensors that can measure the length of the robot's cables. In particular, an iterative optimization process is proposed to estimate the 6-DoF poses of each rigid element of a tensegrity robot from an RGB-D video as well as endcap distance measurements from the cable sensors. To ensure the pose estimates of rigid elements are physically feasible, i.e., they are not resulting in collisions between rods or with the environment, physical constraints are introduced during the optimization. Real-world experiments are performed with a 3-bar tensegrity robot, which performs locomotion gaits. Given ground truth data from a motion capture system, the proposed method achieves less than 1 cm translation error and 3 degrees rotation error, which significantly outperforms alternatives. At the same time, the approach can provide pose estimates throughout the robot's motion, while motion capture often fails due to occlusions. },
keywords = {Robot Perception, tensegrity},
pubstate = {published},
tppubtype = {inproceedings}
}

@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}
}

@inproceedings{yodo_rss22,
title = {You Only Demonstrate Once: Category-Level Manipulation from Single Visual Demonstration},
author = {B Wen and W Lian and K E Bekris and S Schaal},
url = {https://arxiv.org/abs/2201.12716},
year = {2022},
date = {2022-06-29},
booktitle = {Robotics: Science and Systems (RSS)},
abstract = {Promising results have been achieved recently in category-level manipulation that generalizes across object instances. Nevertheless, it often requires expensive real-world data collection and manual specification of semantic keypoints for each object category and task. Additionally, coarse keypoint predictions and ignoring intermediate action sequences hinder adoption in complex manipulation tasks beyond pick-and-place. This work proposes a novel, category-level manipulation framework that leverages an object-centric, category-level representation and model-free 6 DoF motion tracking. The canonical object representation is learned solely in simulation and then used to parse a category-level, task trajectory from a single demonstration video. The demonstration is reprojected to a target trajectory tailored to a novel object via the canonical representation. During execution, the manipulation horizon is decomposed into long range, collision-free motion and last-inch manipulation. For the latter part, a category-level behavior cloning (CatBC) method leverages motion tracking to perform closed-loop control. CatBC follows the target trajectory, projected from the demonstration and anchored to a dynamically selected category-level coordinate frame. The frame is automatically selected along the manipulation horizon by a local attention mechanism. This framework allows to teach different manipulation strategies by solely providing a single demonstration, without complicated manual programming. Extensive experiments demonstrate its efficacy in a range of challenging industrial tasks in high precision assembly, which involve learning complex, long-horizon policies. The process exhibits robustness against uncertainty due to dynamics as well as generalization across object instances and scene configurations. },
note = {Nomination for Best Paper Award},
keywords = {Learning, Manipulation, Robot Perception},
pubstate = {published},
tppubtype = {inproceedings}
}

@inproceedings{reconstruct_lifelong_manipulation,
title = {Online Object Model Reconstruction and Reuse for Lifelong Improvement of Robot Manipulation},
author = {S Lu and R Wang and Y Miao and C Mitash and K E Bekris},
url = {https://arxiv.org/abs/2109.13910},
year = {2022},
date = {2022-05-28},
booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
abstract = {This work proposes a robotic pipeline for picking and constrained placement of objects without geometric shape priors. Compared to recent efforts developed for similar tasks, where every object was assumed to be novel, the proposed system recognizes previously manipulated objects and performs online model reconstruction and reuse. Over a lifelong manipulation process, the system keeps learning features of objects it has interacted with and updates their reconstructed models. Whenever an instance of a previously manipulated object reappears, the system aims to first recognize it and then register its previously reconstructed model given the current observation. This step greatly reduces object shape uncertainty allowing the system to even reason for parts of objects, which are currently not observable. This also results in better manipulation efficiency as it reduces the need for active perception of the target object during manipulation. To get a reusable reconstructed model, the proposed pipeline adopts: i) TSDF for object representation, and ii) a variant of the standard particle filter algorithm for pose estimation and tracking of the partial object model. Furthermore, an effective way to construct and maintain a dataset of manipulated objects is presented. A sequence of real-world manipulation experiments is performed. They show how future manipulation tasks become more effective and efficient by reusing reconstructed models of previously manipulated objects, which were generated during their prior manipulation, instead of treating objects as novel every time. },
note = {Nomination for Best Paper Award in Manipulation},
keywords = {Manipulation, Robot Perception},
pubstate = {published},
tppubtype = {inproceedings}
}

@inproceedings{catgrasp_icra22,
title = {CaTGrasp: Learning Category-Level Task-Relevant Grasping in Clutter from Simulation},
author = {B Wen and W Lian and K E Bekris and S Schaal},
url = {https://arxiv.org/abs/2109.09163},
year = {2022},
date = {2022-05-25},
booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
abstract = {Task-relevant grasping is critical for industrial assembly, where downstream manipulation tasks constrain the set of
valid grasps. Learning how to perform this task, however, is challenging, since task-relevant grasp labels are hard
to define and annotate. There is also yet no consensus on proper representations for modeling or off-the-shelf tools
for performing task-relevant grasps. This work proposes a framework to learn task-relevant grasping for industrial
objects without the need of time-consuming real-world data collection or manual annotation. To achieve this, the
entire framework is trained solely in simulation, including supervised training with synthetic label generation and
self-supervised, hand-object interaction. In the context of this framework, this paper proposes a novel, object-centric canonical representation at the category level, which allows establishing dense correspondence across object instances and transferring task-relevant grasps to novel instances. Extensive experiments on task-relevant grasping of densely-cluttered industrial objects are conducted in both simulation and real-world setups, demonstrating the effectiveness of the proposed framework. Code and data is released at
https://sites.google.com/view/catgrasp. },
keywords = {Manipulation, Robot Perception},
pubstate = {published},
tppubtype = {inproceedings}
}

@inproceedings{bundletrack,
title = {BundleTrack: 6D Pose Tracking for Novel Objects without Instance or Category-Level 3D Models },
author = {B Wen and K E Bekris },
url = {https://arxiv.org/abs/2108.00516},
year = {2021},
date = {2021-09-27},
booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
abstract = {Tracking the 6D pose of objects in video sequences is important for robot manipulation. Prior efforts, however, often assume that the target object's CAD model, at least at a category-level, is available for offline training or during online template matching. This work proposes
BundleTrack, a general framework for 6D pose tracking of novel objects, which does not depend upon instance or category-level 3D models. It leverages the complementary attributes of recent advances in deep learning for segmentation and robust feature extraction, as well as
memory augmented pose-graph optimization for achieving spatiotemporal consistency. This enables long-term, low-drift tracking under various challenging scenarios, including significant occlusions and object motions. Comprehensive experiments given two public benchmarks
demonstrate that the proposed approach significantly outperforms state-of-art category-level 6D tracking or dynamic-SLAM methods. When compared against state-of-art methods that rely on an object instance CAD model, comparable performance is achieved, despite the proposed method's reduced information requirements. An efficient implementation in CUDA provides a real-time performance of 10Hz for the entire framework. },
keywords = {Pose Estimation, Robot Perception},
pubstate = {published},
tppubtype = {inproceedings}
}

@conference{MorWen2021,
title = {Vision-driven Compliant Manipulation for Reliable, High-Precision Assembly Tasks},
author = {A Morgan and B Wen and L Junchi and A Boularias and A Dollar and K E Bekris},
url = {https://arxiv.org/abs/2106.14070},
year = {2021},
date = {2021-07-12},
booktitle = {Robotics: Science and Systems},
abstract = {Highly constrained manipulation tasks continue to be challenging for autonomous robots as they require high levels of precision, typically less than 1mm, which is often incompatible with what can be achieved by traditional perception systems. This paper demonstrates that the combination of state-of-the-art object tracking with passively adaptive mechanical hardware can be leveraged to complete precision manipulation tasks with tight, industrially-relevant tolerances (0.25mm). The proposed control method closes the loop through vision by tracking the relative 6D pose of objects in the relevant workspace. It adjusts the control reference of both the compliant manipulator and the hand to complete object insertion tasks via within-hand manipulation. Contrary to previous efforts for insertion, our method does not require expensive force sensors, precision manipulators, or time-consuming, online learning, which is data hungry. Instead, this effort leverages mechanical compliance and utilizes an object-agnostic manipulation model of the hand learned offline, off-the-shelf motion planning, and an RGBD-based object tracker trained solely with synthetic data. These features allow the proposed system to easily generalize and transfer to new tasks and environments. This paper describes in detail the system components and showcases its efficacy with extensive experiments involving tight tolerance peg-in-hole insertion tasks of various geometries as well as open-world constrained placement tasks.},
keywords = {Manipulation, Robot Perception},
pubstate = {published},
tppubtype = {conference}
}

@conference{207,
title = {Towards Robust Product Packing with a Minimalistic End-Effector},
author = {R Shome and W N Tang and C Song and C Mitash and C Kourtev and J Yu and A Boularias and K E Bekris},
url = {http://robotpacking.org/},
year = {2019},
date = {2019-05-01},
booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
abstract = {Advances in sensor technologies, object detection algorithms, planning frameworks and hardware designs have motivated the deployment of robots in warehouse automation. A variety of such applications, like order fulfillment or packing tasks, require picking objects from unstructured piles and carefully arranging them in bins or containers. Desirable solutions need to be low-cost, easily deployable and controllable, making minimalistic hardware choices desirable. The challenge in designing an effective solution to this problem relates to appropriately integrating multiple components, so as to achieve a robust pipeline that minimizes failure conditions. The current work proposes a complete pipeline for solving such packing tasks, given access only to RGB-D data and a single robot arm with a minimalistic, vacuum-based end-effector. To achieve the desired level of robustness, three key manipulation primitives are identified, which take advantage of the environment and simple operations to successfully pack multiple cubic objects. The overall approach is demonstrated to be robust to execution and perception errors. The impact of each manipulation primitive is evaluated by considering different versions of the proposed pipeline that incrementally introduce reasoning about object poses and corrective manipulation actions.},
note = {Nomination for Best Paper Award in Automation},
keywords = {Manipulation, Robot Perception},
pubstate = {published},
tppubtype = {conference}
}