Andrew Bylard

ASL Publications

S. Schneider, A. Bylard, T. G. Chen, P. Wang, M. R. Cutkosky, and M. Pavone, “ReachBot: A Small Robot for Large Mobile Manipulation Tasks,” in IEEE Aerospace Conference, Big Sky, Montana, 2022.
[BibTeX] [Abstract]

Abstract: Robots are widely deployed in space environments because of their versatility and robustness. However, adverse gravity conditions and challenging terrain geometry expose the limitations of traditional robot designs, which are often forced to sacrifice one of mobility or manipulation capabilities to attain the other. Prospective climbing operations in these environments reveals a need for small, compact robots capable of versatile mobility and manipulation. We propose a novel robotic concept called ReachBot that fills this need by combining two existing technologies: extendable booms and mobile manipulation. ReachBot leverages the reach and tensile strength of extendable booms to achieve an outsized reachable workspace and wrench capability. Through their lightweight, compactable structure, these booms also reduce mass and complexity compared to traditional rigid-link articulated-arm designs. Using these advantages, ReachBot excels in mobile manipulation missions in low gravity or that require climbing, particularly when anchor points are sparse. After introducing the ReachBot concept, we discuss modeling approaches and strategies for increasing stability and robustness. We then develop a 2D analytical model for ReachBotâ€™s dynamics inspired by grasp models for dexterous manipulators. Next, we introduce a waypoint-tracking controller for a planar ReachBot in microgravity. Our simulation results demonstrate the controllerâ€™s robustness to disturbances and modeling error. Finally, we briefly discuss next steps that build on these initially promising results to realize the full potential of ReachBot.
```
@inproceedings{SchneiderBylardEtAl2022,
  author = {Schneider, S. and Bylard, A. and Chen, T. G. and Wang, P. and Cutkosky, M. R. and Pavone, M.},
  title = {{ReachBot:} {A} Small Robot for Large Mobile Manipulation Tasks},
  booktitle = {{IEEE Aerospace Conference}},
  year = {2022},
  address = {Big Sky, Montana},
  month = mar,
  url = {https://arxiv.org/abs/2110.10829},
  keywords = {pub},
  owner = {schneids},
  timestamp = {2021-11-04}
}
```
M. Pavone, M. Cutkosky, M. Lapôtre, S. Schneider, T. G. Chen, and A. Bylard, “ReachBot: a Small Robot for Large Mobile Manipulation Tasks in Martian Cave Environments,” NASA NIAC Program, 2022.
[BibTeX] [Abstract]

Abstract: This study investigated a novel mission architecture where a long-reach crawling and anchoring robot, which repurposes extendable booms for mobile manipulation, is deployed to explore and sample difficult terrains on solar system bodies, with a key focus on Mars exploration. To this end, the robot concept introduced by this effort, called ReachBot, uses rollable extendable booms as manipulator arms and as highly reconfigurable structural members. ReachBot is capable of (1) rapid and versatile crawling through sequences of long-distance grasps, (2) traversing a large workspace while anchored (by adjusting boom lengths and orientations), and (3) applying high interaction forces and torques, primarily leveraging boom tensile strength and the variety of anchors within reach. These features allow a light and compact robot to achieve versatile mobility and forceful interaction in traditionally difficult environments such as vertical cliff walls or the rocky and uneven interiors of caves on Mars (see figure, left). In particular, ReachBot is uniquely suited for exploring and sampling Noachian targets on Mars that contain key sources of historical and astrobiological information preserved in strata in the form of cliff-face fractures and sublimation pits [1]. To develop this concept, this Phase I study brought together an interdisciplinary team of experts in robot autonomy, robotic manipulation, mechanical design, bio-inspired grasping, and geological planetary science from Stanford.
```
@techreport{PavoneCutkoskyEtAl2012,
  author = {Pavone, M. and Cutkosky, M. and Lap\^{o}tre, M. and Schneider, S. and Chen, T. G. and Bylard, A.},
  title = {ReachBot: a Small Robot for Large Mobile Manipulation Tasks in Martian Cave Environments},
  institution = {{NASA NIAC Program}},
  year = {2022},
  note = {Final report},
  owner = {schneids},
  timestamp = {2022-10-14},
  url = {/wp-content/papercite-data/pdf/Pavone.ea.NIAC.Final.Report.2022.pdf}
}
```
T. Lew, A. Sharma, J. Harrison, A. Bylard, and M. Pavone, “Safe Active Dynamics Learning and Control: A Sequential Exploration-Exploitation Framework,” IEEE Transactions on Robotics, vol. 38, no. 5, pp. 2888–2907, 2022.
[BibTeX] [Abstract]

Abstract: To safely deploy learning-based systems in highly uncertain environments, one must ensure that they always satisfy constraints. In this work, we propose a practical and theoretically justified approach to maintaining safety in the presence of dynamics uncertainty. Our approach leverages Bayesian meta-learning with last-layer adaptation: the expressiveness of neural-network features trained offline, paired with efficient last-layer online adaptation, enables the derivation of tight confidence sets which contract around the true dynamics as the model adapts online. We exploit these confidence sets to plan trajectories that guarantee the safety of the system. Our approach handles problems with high dynamics uncertainty where reaching the goal safely is initially infeasible by first exploring to gather data and reduce uncertainty, before autonomously exploiting the acquired information to safely perform the task. Under reasonable assumptions, we prove that our framework provides safety guarantees in the form of a single joint chance constraint. Furthermore, we use this theoretical analysis to motivate regularization of the model to improve performance. We extensively demonstrate our approach in simulation and on hardware.
```
@article{LewEtAl2022,
  author = {Lew, T. and Sharma, A. and Harrison, J. and Bylard, A. and Pavone, M.},
  title = {Safe Active Dynamics Learning and Control: A Sequential Exploration-Exploitation Framework},
  journal = {{IEEE Transactions on Robotics}},
  volume = {38},
  number = {5},
  pages = {2888--2907},
  booktitle = {{Proc. Conf. on Uncertainty in Artificial Intelligence}},
  year = {2022},
  doi = {10.1109/TRO.2022.3154715},
  url = {https://arxiv.org/pdf/2008.11700.pdf},
  owner = {lew},
  timestamp = {2022-01-27}
}
```
T. G. Chen, B. Miller, C. Winston, S. Schneider, A. Bylard, M. Pavone, and M. R. Cutkosky, “ReachBot: A Small Robot with Exceptional Reach for Rough Terrain,” in Proc. IEEE Conf. on Robotics and Automation, 2022.
[BibTeX] [Abstract]

Abstract: ReachBot is a new concept for planetary exploration, consisting of a small body and long, lightweight extending arms loaded primarily in tension. The arms are equipped with spined grippers for anchoring on rock surfaces. The design and testing of a planar prototype is presented here. Experiments with rock grasping and coordinated locomotion illustrate the advantages of low inertia passive grippers, triggered by impact and using stored mechanical energy for the internal force. Gripper design involves a trade-off among the range of possible grasp angles, maximum grasp force, required triggering force, and required reset force. The current prototype can pull with up to 8 N when gripping volcanic rock, limited only by the strength of the 3D printed components. Calculations predict a maximum pull of 26 N for the same spines and stronger materials.
```
@inproceedings{ChenMillerEtAl2022,
  author = {Chen, T. G. and Miller, B. and Winston, C. and Schneider, S. and Bylard, A. and Pavone, M. and Cutkosky, M. R.},
  title = {{ReachBot:} {A} Small Robot with Exceptional Reach for Rough Terrain},
  booktitle = {{Proc. IEEE Conf. on Robotics and Automation}},
  year = {2022},
  url = {/wp-content/papercite-data/pdf/Chen.Miller.ea.RAL22.pdf},
  keywords = {pub},
  owner = {bylard},
  timestamp = {2021-12-09}
}
```
A. Bylard, “Leveraging the Geometric Structure of Robotic Tasks for Motion Design,” PhD thesis, Stanford University, Dept. of Aeronautics and Astronautics, Stanford, California, 2021.
[BibTeX] [Abstract]

Abstract: Robots operating in the unstructured environments of the real world must contend with at least two sources of geometric complexity: (1) the differential geometric complexity of robot configuration spaces and task spaces, which can in practice be general non-Euclidean manifolds, and (2) the complexity of the geometric shape of robot links and obstacles in the environment, which have infinite variability and are often highly nonconvex. Mature robot autonomy requires algorithms that can tackle these sources of geometric complexity with precision and at real-time control and planning speeds. This thesis focuses on bridging existing gaps in previous methods to meet these needs. In particular, to address differential geometric complexity in motion design, we first present a framework called Multi-Task Pullback Bundle Dynamical Systems (PBDS), which is a geometric control methodology for forming fast composable geometric motion policies, respecting simultaneous robotic tasks on non-Euclidean robot and task manifolds. Second, we present an embedded sequential convex programming approach which exploits differential geometric structure to eliminate explicit manifold-type constraints in trajectory optimization while still guaranteeing final satisfaction of these constraints. Together, these approaches correctly enforce the differential geometric structure of robot planning and control problems while maintaining similar or greater computational efficiency compared to past approaches. Finally, we address the shape complexity of robot links and obstacles by repurposing hardware-accelerated ray-tracing (i.e., ray-tracing cores) for rapidly forming collision avoidance constraints in trajectory optimization, particularly targeting complex robot and obstacle triangle mesh representations. This enables robot motion designers to leverage the full complexity of robot and obstacle geometries at speeds orders of magnitude faster than currently available collision-checking libraries, while leveraging recently-developed ray-tracing cores which have previously had little utility in the robot autonomy stack.
```
@phdthesis{Bylard2021,
  author = {Bylard, A.},
  title = {Leveraging the Geometric Structure of Robotic Tasks for Motion Design},
  school = {{Stanford University, Dept. of Aeronautics and Astronautics}},
  year = {2021},
  address = {Stanford, California},
  month = dec,
  url = {https://stacks.stanford.edu/file/druid:jw453mh1486/BylardPhDThesis-augmented.pdf},
  owner = {bylard},
  timestamp = {2021-12-06}
}
```
A. Bylard, R. Bonalli, and M. Pavone, “Composable Geometric Motion Policies using Multi-Task Pullback Bundle Dynamical Systems,” in Proc. IEEE Conf. on Robotics and Automation, Xi’an, China, 2021.
[BibTeX] [Abstract]

Abstract: Despite decades of work in fast reactive planning and control, challenges remain in developing reactive motion policies on non-Euclidean manifolds and enforcing constraints while avoiding undesirable potential function local minima. This work presents a principled method for designing and fusing desired robot task behaviors into a stable robot motion policy, leveraging the geometric structure of non-Euclidean manifolds, which are prevalent in robot configuration and task spaces. Our Pullback Bundle Dynamical Systems (PBDS) framework drives desired task behaviors and prioritizes tasks using separate position-dependent and position/velocity-dependent Riemannian metrics, respectively, thus simplifying individual task design and modular composition of tasks. For enforcing constraints, we provide a class of metric-based tasks, eliminating local minima by imposing non-conflicting potential functions only for goal region attraction. We also provide a geometric optimization problem for combining tasks inspired by Riemannian Motion Policies (RMPs) that reduces to a simple least-squares problem, and we show that our approach is geometrically well-defined. We demonstrate the PBDS framework on the sphere S2 and at 300-500 Hz on a manipulator arm, and we provide task design guidance and an open-source Julia library implementation. Overall, this work presents a fast, easy-to-use framework for generating motion policies without unwanted potential function local minima on general manifolds.
```
@inproceedings{BylardBonalliEtAl2021,
  author = {Bylard, A. and Bonalli, R. and Pavone, M.},
  title = {Composable Geometric Motion Policies using Multi-Task Pullback Bundle Dynamical Systems},
  booktitle = {{Proc. IEEE Conf. on Robotics and Automation}},
  year = {2021},
  address = {Xi'an, China},
  month = may,
  owner = {jthluke},
  timestamp = {2024-10-28},
  url = {https://arxiv.org/abs/2101.01297}
}
```
M. Mote, M. Egerstedt, E. Feron, A. Bylard, and M. Pavone, “Collision-Inclusive Trajectory Optimization for Free-Flying Spacecraft,” AIAA Journal of Guidance, Control, and Dynamics, vol. 43, no. 7, pp. 1247–1258, 2020.
[BibTeX] [Abstract]

Abstract: In systems where collisions can be tolerated, permitting and optimizing collisions in vehicle trajectories can enable a richer set of possible behaviors, allowing both better performance and determination of safest courses of action in scenarios where collision is inevitable. This paper develops an approach for optimal trajectory planning on a three degree-of-freedom free-flying spacecraft having tolerance to collisions. First, we use experimental data to formulate a physically realistic collision model for the spacecraft. We show that this model is linear over the expected operational range, enabling a piecewise affine representation of the full hybrid-vehicle dynamics. Next, we incorporate this dynamics model along with vehicle constraints into a mixed integer program. Experimental comparisons of trajectories with and without collision-avoidance requirements demonstrate the capability of the collision-tolerant strategy to achieve significant performance improvements in realistic scenarios. A simulated case study illustrates the potential for this approach to find damage-mitigating paths in online implementations.
```
@article{MoteEgerstedtEtAl2020,
  author = {Mote, M. and Egerstedt, M. and Feron, E. and Bylard, A. and Pavone, M.},
  title = {Collision-Inclusive Trajectory Optimization for Free-Flying Spacecraft},
  journal = {{AIAA Journal of Guidance, Control, and Dynamics}},
  volume = {43},
  number = {7},
  pages = {1247-1258},
  year = {2020},
  url = {/wp-content/papercite-data/pdf/Mote.ea.JGCD.2020.preprint.pdf},
  owner = {bylard},
  timestamp = {2021-10-14}
}
```
R. Bonalli, A. Bylard, A. Cauligi, T. Lew, and M. Pavone, “Trajectory Optimization on Manifolds: A Theoretically-Guaranteed Embedded Sequential Convex Programming Approach,” in Robotics: Science and Systems, Freiburg im Breisgau, Germany, 2019.
[BibTeX] [Abstract]

Abstract: Sequential Convex Programming (SCP) has recently gain popularity as a tool for trajectory optimization, due to its sound theoretical properties and practical performance. Yet, most SCP-based methods for trajectory optimization are restricted to Euclidean settings, which precludes their application to problem instances where one needs to reason about manifold-type constraints (that is, constraints, such as loop closure, which restrict the motion of a system to a subset of the ambient space). The aim of this paper is to fill this gap by extending SCP-based trajectory optimization methods to a manifold setting. The key insight is to leverage geometric embeddings to lift a manifold-constrained trajectory optimization problem into an equivalent problem defined over a space enjoying Euclidean structure. This insight allows one to extend existing SCP methods to a manifold setting in a fairly natural way. In particular, we present an SCP algorithm for manifold problems with theoretical guarantees that resemble those derived for the Euclidean setting, and demonstrate its practical performance via numerical experiments.
```
@inproceedings{BonalliBylardEtAl2019,
  author = {Bonalli, R. and Bylard, A. and Cauligi, A. and Lew, T. and Pavone, M.},
  title = {Trajectory Optimization on Manifolds: {A} Theoretically-Guaranteed Embedded Sequential Convex Programming Approach},
  booktitle = {{Robotics: Science and Systems}},
  year = {2019},
  address = {Freiburg im Breisgau, Germany},
  month = jun,
  url = {https://arxiv.org/pdf/1905.07654.pdf},
  owner = {bylard},
  timestamp = {2019-05-01}
}
```
R. Bonalli, A. Cauligi, A. Bylard, and M. Pavone, “GuSTO: Guaranteed Sequential Trajectory Optimization via Sequential Convex Programming,” in Proc. IEEE Conf. on Robotics and Automation, Montreal, Canada, 2019.
[BibTeX] [Abstract]

Abstract: Sequential Convex Programming (SCP) has recently seen a surge of interest as a tool for trajectory optimization. Yet, most available methods lack rigorous performance guarantees and are often tailored to specific optimal control setups. In this paper, we present GuSTO (Guaranteed Sequential Trajectory Optimization), an algorithmic framework to solve trajectory optimization problems for control-affine systems with drift. GuSTO generalizes earlier SCP-based methods for trajectory optimization (by addressing, for example, goal region constraints and problems with either fixed or free final time), and enjoys theoretical convergence guarantees in terms of convergence to, at least, a stationary point. The theoretical analysis is further leveraged to devise an accelerated implementation of GuSTO, which originally infuses ideas from indirect optimal control into an SCP context. Numerical experiments on a variety of trajectory optimization setups show that GuSTO generally outperforms current state-of-the-art approaches in terms of success rates, solution quality, and computation times.
```
@inproceedings{BonalliCauligiEtAl2019,
  author = {Bonalli, R. and Cauligi, A. and Bylard, A. and Pavone, M.},
  title = {{GuSTO:} Guaranteed Sequential Trajectory Optimization via Sequential Convex Programming},
  booktitle = {{Proc. IEEE Conf. on Robotics and Automation}},
  year = {2019},
  address = {Montreal, Canada},
  month = may,
  url = {https://arxiv.org/pdf/1903.00155.pdf},
  owner = {bylard},
  timestamp = {2018-10-04}
}
```
T. Zahroof, A. Bylard, H. Shageer, and M. Pavone, “Perception-Constrained Robot Manipulator Planning for Satellite Servicing,” in IEEE Aerospace Conference, Big Sky, Montana, 2019.
[BibTeX] [Abstract]

Abstract: Satellite servicing is a rapidly developing industry which requires a number advances in semi- and fully-automated space robotics to unlock many key servicing capabilities. One upcoming mission example is the NASA Restore-L Robotic Servicing spacecraft, which is equipped with two 7-joint robotic manipulators used to capture a satellite and perform a complex series of refueling tasks, including swapping between various end-effector tools stored on board. In this scenario, planning of the manipulator motions must account for a number of constraints, such as collision avoidance and the potential need for uninterrupted visual tracking of objects or of the end-effector. Such complex constraints in a cluttered environment, such as the interface between two spacecraft, are time-consuming to incorporate into hand-designed trajectories. Thus, in this work we present a software tool which uses robot motion planning and path refinement algorithms for automated, real-time computation of near-optimal, collision-free trajectories which satisfy the aforementioned perception constraints. The tool is built on the ROS MoveIt! framework, which can simulate and visualize trajectories as well as seamlessly switch between motion planning and refinement algorithms depending on task requirements. Additionally, we performed experimental campaigns to benchmark a number of available algorithms for performance in handling such perception constraints. Although the framework is applied to a mock-up of Restore-L satellite servicer in this paper, the tool can be applied to any fixed-base manipulator planning scenario with a similar class of constraints.
```
@inproceedings{ZahroofBylardEtAl2019,
  author = {Zahroof, T. and Bylard, A. and Shageer, H. and Pavone, M.},
  title = {Perception-Constrained Robot Manipulator Planning for Satellite Servicing},
  booktitle = {{IEEE Aerospace Conference}},
  year = {2019},
  address = {Big Sky, Montana},
  month = mar,
  url = {/wp-content/papercite-data/pdf/Zahroof.Bylard.Shageer.Pavone.AeroConf19.pdf},
  owner = {bylard},
  timestamp = {2019-01-14}
}
```
R. MacPherson, B. Hockman, A. Bylard, M. A. Estrada, M. R. Cutkosky, and M. Pavone, “Trajectory Optimization for Dynamic Grasping in Space using Adhesive Grippers,” in Field and Service Robotics, Zurich, Switzerland, 2017.
[BibTeX] [Abstract]

Abstract: Spacecraft equipped with gecko-inspired dry adhesive grippers can dynamically grasp objects having a wide variety of featureless surfaces. In this paper we propose an optimization-based control strategy to exploit the dynamic robustness of such grippers for the task of grasping a free-floating, spinning object. First, we extend previous work characterizing the dynamic grasping capabilities of these grippers to the case where both object and spacecraft are free-floating and comparably sized. We then formulate the acquisition problem as a two-phase optimal control problem, which is amenable to real time implementation and can handle constraints on velocity, control, as well as integer timing constraints for grasping a specific target location on the surface of a spinning object. Conservative analytical bounds on the set of initial states that guarantee persistent feasibility are derived.
```
@inproceedings{MacPhersonHockmanEtAl2017,
  author = {MacPherson, R. and Hockman, B. and Bylard, A. and Estrada, M. A. and Cutkosky, M. R. and Pavone, M.},
  title = {Trajectory Optimization for Dynamic Grasping in Space using Adhesive Grippers},
  booktitle = {{Field and Service Robotics}},
  year = {2017},
  address = {Zurich, Switzerland},
  month = sep,
  url = {/wp-content/papercite-data/pdf/MacPherson.Hockman.Bylard.ea.FSR17.pdf},
  owner = {bylard},
  timestamp = {2018-01-16}
}
```
A. Bylard, R. MacPherson, B. Hockman, M. R. Cutkosky, and M. Pavone, “Robust Capture and Deorbit of Rocket Body Debris Using Controllable Dry Adhesion,” in IEEE Aerospace Conference, Big Sky, Montana, 2017.
[BibTeX] [Abstract]

Abstract: Removing large orbital debris in a safe, robust, and cost-effective manner is a long-standing challenge, having serious implications for LEO satellite safety and access to space. Many studies have focused on the deorbit of spent rocket bodies (R/Bs) as an achievable and high-priority first step. However, major difficulties arise from the R/Bs’ residual tumble and lack of traditional docking/grasping fixtures. Previously investigated docking strategies often require complex and risky approach maneuvers or have a high chance of producing additional debris. To address this challenge, this paper investigates the use of controllable dry adhesives (CDAs), also known as gecko-inspired adhesives, as an alternative approach to R/B docking and deorbiting. CDAs are gathering interest for in-space grasping and manipulation due to their ability to controllably attach to and detach from any smooth, clean surface, including flat and curved surfaces. Such capability significantly expands the number and types of potential docking locations on a target. CDAs are also inexpensive, are space-qualified (performing well in a vacuum, in extreme temperatures, and under radiation), and can attach and detach while applying minimal force to a target surface, all important considerations for space deployment. In this paper, we investigate a notional strategy for initial capture and stabilization of a R/B having multi-axis tumble, exploiting the unique properties of CDA grippers to reduce maneuver complexity, and we propose alternatives for rigidly attaching deorbiting kits to a R/B. Simulations based on experimentally verified models of CDA grippers show that these approaches show promise as robust alternatives to previously explored methods.
```
@inproceedings{BylardMacPhersonEtAl2017,
  author = {Bylard, A. and MacPherson, R. and Hockman, B. and Cutkosky, M. R. and Pavone, M.},
  title = {Robust Capture and Deorbit of Rocket Body Debris Using Controllable Dry Adhesion},
  booktitle = {{IEEE Aerospace Conference}},
  year = {2017},
  address = {Big Sky, Montana},
  month = mar,
  url = {/wp-content/papercite-data/pdf/Bylard.MacPherson.Hockman.ea.AeroConf17.pdf},
  owner = {bylard},
  timestamp = {2017-03-07}
}
```
M. A. Estrada, B. Hockman, A. Bylard, E. W. Hawkes, M. R. Cutkosky, and M. Pavone, “Free-Flyer Acquisition of Spinning Objects with Gecko-Inspired Adhesives,” in Proc. IEEE Conf. on Robotics and Automation, Stockholm, Sweden, 2016.
[BibTeX] [Abstract]

Abstract: We explore the use of grippers with gecko-inspired adhesives for spacecraft docking and acquisition of tumbling objects in microgravity. Towards the goal of autonomous object manipulation in space, adhesive grippers mounted on planar free-floating platforms are shown to be tolerant of a range of incoming linear and angular velocities. Through modeling, simulations, and experiments, we characterize the dynamic “grasping envelope” for successful acquisition and derive insights to inform future gripper designs and grasping strategies for motion planning.
```
@inproceedings{EstradaHockmanEtAl2016,
  author = {Estrada, M. A. and Hockman, B. and Bylard, A. and Hawkes, E. W. and Cutkosky, M. R. and Pavone, M.},
  title = {Free-Flyer Acquisition of Spinning Objects with Gecko-Inspired Adhesives},
  booktitle = {{Proc. IEEE Conf. on Robotics and Automation}},
  year = {2016},
  address = {Stockholm, Sweden},
  doi = {10.1109/ICRA.2016.7487696},
  month = may,
  url = {/wp-content/papercite-data/pdf/Estrada.Hockman.Bylard.ea.ICRA16.pdf},
  owner = {bylard},
  timestamp = {2017-01-28}
}
```

Contacts:

Andrew Bylard

Awards:

ASL Publications