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\n  \n 2021\n \n \n (7)\n \n \n
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\n \n\n \n \n \n \n \n \n Interactive multi-modal motion planning with Branch Model Predictive Control.\n \n \n \n \n\n\n \n Chen, Y.; Rosolia, U.; Ubellacker, W.; Csomay-Shanklin, N.; and Ames, A. D.\n\n\n \n\n\n\n arXiv:2109.05128 [cs, eess]. September 2021.\n arXiv: 2109.05128\n\n\n\n
\n\n\n\n \n \n \"InteractivePaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
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@article{chen_interactive_2021,\n\ttitle = {Interactive multi-modal motion planning with {Branch} {Model} {Predictive} {Control}},\n\turl = {http://arxiv.org/abs/2109.05128},\n\tabstract = {Motion planning for autonomous robots and vehicles in presence of uncontrolled agents remains a challenging problem as the reactive behaviors of the uncontrolled agents must be considered. Since the uncontrolled agents usually demonstrate multimodal reactive behavior, the motion planner needs to solve a continuous motion planning problem under these behaviors, which contains a discrete element. We propose a branch Model Predictive Control (MPC) framework that plans over feedback policies to leverage the reactive behavior of the uncontrolled agent. In particular, a scenario tree is constructed from a finite set of policies of the uncontrolled agent, and the branch MPC solves for a feedback policy in the form of a trajectory tree, which shares the same topology as the scenario tree. Moreover, coherent risk measures such as the Conditional Value at Risk (CVaR) are used as a tuning knob to adjust the tradeoff between performance and robustness. The proposed branch MPC framework is tested on an overtake and lane change task and a merging task for autonomous vehicles in simulation, and on the motion planning of an autonomous quadruped robot alongside an uncontrolled quadruped in experiments. The result demonstrates interesting human-like behaviors, achieving a balance between safety and performance.},\n\tlanguage = {en},\n\turldate = {2022-03-16},\n\tjournal = {arXiv:2109.05128 [cs, eess]},\n\tauthor = {Chen, Yuxiao and Rosolia, Ugo and Ubellacker, Wyatt and Csomay-Shanklin, Noel and Ames, Aaron D.},\n\tmonth = sep,\n\tyear = {2021},\n\tnote = {arXiv: 2109.05128},\n\tkeywords = {Computer Science - Robotics, Electrical Engineering and Systems Science - Systems and Control},\n}\n\n
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\n Motion planning for autonomous robots and vehicles in presence of uncontrolled agents remains a challenging problem as the reactive behaviors of the uncontrolled agents must be considered. Since the uncontrolled agents usually demonstrate multimodal reactive behavior, the motion planner needs to solve a continuous motion planning problem under these behaviors, which contains a discrete element. We propose a branch Model Predictive Control (MPC) framework that plans over feedback policies to leverage the reactive behavior of the uncontrolled agent. In particular, a scenario tree is constructed from a finite set of policies of the uncontrolled agent, and the branch MPC solves for a feedback policy in the form of a trajectory tree, which shares the same topology as the scenario tree. Moreover, coherent risk measures such as the Conditional Value at Risk (CVaR) are used as a tuning knob to adjust the tradeoff between performance and robustness. The proposed branch MPC framework is tested on an overtake and lane change task and a merging task for autonomous vehicles in simulation, and on the motion planning of an autonomous quadruped robot alongside an uncontrolled quadruped in experiments. The result demonstrates interesting human-like behaviors, achieving a balance between safety and performance.\n
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\n \n\n \n \n \n \n \n Episodic Learning for Safe Bipedal Locomotion with Control Barrier Functions and Projection-to-State Safety.\n \n \n \n\n\n \n Csomay-Shanklin, N.; Cosner, R. K; Dai, M.; Taylor, A. J; and Ames, A. D\n\n\n \n\n\n\n ,13. June 2021.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 10 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{csomay-shanklin_episodic_2021,\n\ttitle = {Episodic {Learning} for {Safe} {Bipedal} {Locomotion} with {Control} {Barrier} {Functions} and {Projection}-to-{State} {Safety}},\n\tabstract = {This paper combines episodic learning and control barrier functions in the setting of bipedal locomotion. The safety guarantees that control barrier functions provide are only valid with perfect model knowledge; however, this assumption cannot be met on hardware platforms. To address this, we utilize the notion of projection-to-state safety paired with a machine learning framework in an attempt to learn the model uncertainty as it affects the barrier functions. The proposed approach is demonstrated both in simulation and on hardware for the AMBER-3M bipedal robot in the context of the stepping-stone problem, which requires precise foot placement while walking dynamically.},\n\tlanguage = {en},\n\tauthor = {Csomay-Shanklin, Noel and Cosner, Ryan K and Dai, Min and Taylor, Andrew J and Ames, Aaron D},\n\tmonth = jun,\n\tyear = {2021},\n\tpages = {13},\n}\n\n
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\n This paper combines episodic learning and control barrier functions in the setting of bipedal locomotion. The safety guarantees that control barrier functions provide are only valid with perfect model knowledge; however, this assumption cannot be met on hardware platforms. To address this, we utilize the notion of projection-to-state safety paired with a machine learning framework in an attempt to learn the model uncertainty as it affects the barrier functions. The proposed approach is demonstrated both in simulation and on hardware for the AMBER-3M bipedal robot in the context of the stepping-stone problem, which requires precise foot placement while walking dynamically.\n
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\n \n\n \n \n \n \n \n \n Online Learning of Unknown Dynamics for Model-Based Controllers in Legged Locomotion.\n \n \n \n \n\n\n \n Sun, Y.; Ubellacker, W. L.; Ma, W.; Zhang, X.; Wang, C.; Csomay-Shanklin, N. V.; Tomizuka, M.; Sreenath, K.; and Ames, A. D.\n\n\n \n\n\n\n IEEE Robotics and Automation Letters, 6(4): 8442–8449. October 2021.\n \n\n\n\n
\n\n\n\n \n \n \"OnlinePaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 14 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{sun_online_2021,\n\ttitle = {Online {Learning} of {Unknown} {Dynamics} for {Model}-{Based} {Controllers} in {Legged} {Locomotion}},\n\tvolume = {6},\n\tissn = {2377-3766, 2377-3774},\n\turl = {https://ieeexplore.ieee.org/document/9525285/},\n\tdoi = {10.1109/LRA.2021.3108510},\n\tabstract = {The performance of a model-based controller can severely suffer when its model inaccurately represents the real world dynamics. We propose to learn a time-varying, locally linear residual model along the robot’s current trajectory, to compensate for the prediction errors of the controller’s model. Supervised learning is performed online, as the robot is running in the unknown environment, using data collected from its immediate past. We theoretically investigate our method in its general formulation, then apply it to a bipedal controller derived from the full-order dynamics of virtual constraints, and a quadrupedal controller derived from a simplified model of contact forces. For a biped in simulation, our method consistently outperforms the baseline and a recent learning-based method. We also experiment with a 12 kg quadruped in simulation and real world, where the baseline fails to walk with 10 kg of payload but our method succeeds.},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2021-12-14},\n\tjournal = {IEEE Robotics and Automation Letters},\n\tauthor = {Sun, Yu and Ubellacker, Wyatt L. and Ma, Wen-Loong and Zhang, Xiang and Wang, Changhao and Csomay-Shanklin, Noel V. and Tomizuka, Masayoshi and Sreenath, Koushil and Ames, Aaron D.},\n\tmonth = oct,\n\tyear = {2021},\n\tpages = {8442--8449},\n}\n\n
\n
\n\n\n
\n The performance of a model-based controller can severely suffer when its model inaccurately represents the real world dynamics. We propose to learn a time-varying, locally linear residual model along the robot’s current trajectory, to compensate for the prediction errors of the controller’s model. Supervised learning is performed online, as the robot is running in the unknown environment, using data collected from its immediate past. We theoretically investigate our method in its general formulation, then apply it to a bipedal controller derived from the full-order dynamics of virtual constraints, and a quadrupedal controller derived from a simplified model of contact forces. For a biped in simulation, our method consistently outperforms the baseline and a recent learning-based method. We also experiment with a 12 kg quadruped in simulation and real world, where the baseline fails to walk with 10 kg of payload but our method succeeds.\n
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\n \n\n \n \n \n \n \n \n A Machine Learning Strategy for Locomotion Classification and Parameter Estimation Using Fusion of Wearable Sensors.\n \n \n \n \n\n\n \n Camargo, J.; Flanagan, W.; Csomay-Shanklin, N.; Kanwar, B.; and Young, A.\n\n\n \n\n\n\n IEEE Transactions on Biomedical Engineering, 68(5): 1569–1578. May 2021.\n \n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{camargo_machine_2021,\n\ttitle = {A {Machine} {Learning} {Strategy} for {Locomotion} {Classification} and {Parameter} {Estimation} {Using} {Fusion} of {Wearable} {Sensors}},\n\tvolume = {68},\n\tissn = {0018-9294, 1558-2531},\n\turl = {https://ieeexplore.ieee.org/document/9376982/},\n\tdoi = {10.1109/TBME.2021.3065809},\n\tabstract = {The accurate classification of ambulation modes and estimation of walking parameters is a challenging problem that is key to many applications. Knowledge of the user’s state can enable rehabilitative devices to adapt to changing conditions, while in a clinical setting it can provide physicians with more detailed patient activity information. This study describes the development and optimization process of a combined locomotion mode classifier and environmental parameter estimator using machine learning and wearable sensors. A detailed analysis of the best sensor types and placements for each problem is also presented to provide device designers with information on which sensors to prioritize for their application. For this study, 15 able-bodied subjects were unilaterally instrumented with inertial measurement unit, goniometer, and electromyography sensors and data were collected for extensive ranges of levelground, ramp, and stair walking conditions. The proposed system classifies steady state ambulation modes with 99\\% accuracy and ambulation mode transitions with 96\\% accuracy, along with estimating ramp incline within 1.25 degrees, stair height within 1.29 centimeters, and walking speed within 0.04 meters per second. Mechanical sensors (inertial measurement units, goniometers) are found to be most important for classification, while goniometers dominate ramp incline and stair height estimation, and speed estimation is performed largely with a single inertial measurement unit. The feature tables and Matlab code to replicate the study are published as supplemental materials.},\n\tlanguage = {en},\n\tnumber = {5},\n\turldate = {2021-12-14},\n\tjournal = {IEEE Transactions on Biomedical Engineering},\n\tauthor = {Camargo, Jonathan and Flanagan, Will and Csomay-Shanklin, Noel and Kanwar, Bharat and Young, Aaron},\n\tmonth = may,\n\tyear = {2021},\n\tpages = {1569--1578},\n}\n\n
\n
\n\n\n
\n The accurate classification of ambulation modes and estimation of walking parameters is a challenging problem that is key to many applications. Knowledge of the user’s state can enable rehabilitative devices to adapt to changing conditions, while in a clinical setting it can provide physicians with more detailed patient activity information. This study describes the development and optimization process of a combined locomotion mode classifier and environmental parameter estimator using machine learning and wearable sensors. A detailed analysis of the best sensor types and placements for each problem is also presented to provide device designers with information on which sensors to prioritize for their application. For this study, 15 able-bodied subjects were unilaterally instrumented with inertial measurement unit, goniometer, and electromyography sensors and data were collected for extensive ranges of levelground, ramp, and stair walking conditions. The proposed system classifies steady state ambulation modes with 99% accuracy and ambulation mode transitions with 96% accuracy, along with estimating ramp incline within 1.25 degrees, stair height within 1.29 centimeters, and walking speed within 0.04 meters per second. Mechanical sensors (inertial measurement units, goniometers) are found to be most important for classification, while goniometers dominate ramp incline and stair height estimation, and speed estimation is performed largely with a single inertial measurement unit. The feature tables and Matlab code to replicate the study are published as supplemental materials.\n
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\n \n\n \n \n \n \n \n \n Preference-Based Learning for User-Guided HZD Gait Generation on Bipedal Walking Robots.\n \n \n \n \n\n\n \n Tucker, M.; Csomay-Shanklin, N.; Ma, W.; and Ames, A. D.\n\n\n \n\n\n\n arXiv:2011.05424 [cs]. March 2021.\n arXiv: 2011.05424\n\n\n\n
\n\n\n\n \n \n \"Preference-BasedPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 17 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{tucker_preference-based_2021,\n\ttitle = {Preference-{Based} {Learning} for {User}-{Guided} {HZD} {Gait} {Generation} on {Bipedal} {Walking} {Robots}},\n\turl = {http://arxiv.org/abs/2011.05424},\n\tabstract = {This paper presents a framework that leverages both control theory and machine learning to obtain stable and robust bipedal locomotion without the need for manual parameter tuning. Traditionally, gaits are generated through trajectory optimization methods and then realized experimentally — a process that often requires extensive tuning due to differences between the models and hardware. In this work, the process of gait realization via hybrid zero dynamics (HZD) based optimization is formally combined with preference-based learning to systematically realize dynamically stable walking. Importantly, this learning approach does not require a carefully constructed reward function, but instead utilizes human pairwise preferences. The power of the proposed approach is demonstrated through two experiments on a planar biped AMBER-3M: the first with rigid point-feet, and the second with induced model uncertainty through the addition of springs where the added compliance was not accounted for in the gait generation or in the controller. In both experiments, the framework achieves stable, robust, efficient, and natural walking in fewer than 50 iterations with no reliance on a simulation environment. These results demonstrate a promising step in the unification of control theory and learning.},\n\tlanguage = {en},\n\turldate = {2021-12-14},\n\tjournal = {arXiv:2011.05424 [cs]},\n\tauthor = {Tucker, Maegan and Csomay-Shanklin, Noel and Ma, Wen-Loong and Ames, Aaron D.},\n\tmonth = mar,\n\tyear = {2021},\n\tnote = {arXiv: 2011.05424},\n\tkeywords = {Computer Science - Machine Learning, Computer Science - Robotics},\n}\n\n
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\n This paper presents a framework that leverages both control theory and machine learning to obtain stable and robust bipedal locomotion without the need for manual parameter tuning. Traditionally, gaits are generated through trajectory optimization methods and then realized experimentally — a process that often requires extensive tuning due to differences between the models and hardware. In this work, the process of gait realization via hybrid zero dynamics (HZD) based optimization is formally combined with preference-based learning to systematically realize dynamically stable walking. Importantly, this learning approach does not require a carefully constructed reward function, but instead utilizes human pairwise preferences. The power of the proposed approach is demonstrated through two experiments on a planar biped AMBER-3M: the first with rigid point-feet, and the second with induced model uncertainty through the addition of springs where the added compliance was not accounted for in the gait generation or in the controller. In both experiments, the framework achieves stable, robust, efficient, and natural walking in fewer than 50 iterations with no reliance on a simulation environment. These results demonstrate a promising step in the unification of control theory and learning.\n
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\n \n\n \n \n \n \n \n \n Verifying Safe Transitions between Dynamic Motion Primitives on Legged Robots.\n \n \n \n \n\n\n \n Ubellacker, W.; Csomay-Shanklin, N.; Molnar, T. G.; and Ames, A. D.\n\n\n \n\n\n\n arXiv:2106.10310 [cs]. June 2021.\n arXiv: 2106.10310\n\n\n\n
\n\n\n\n \n \n \"VerifyingPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 20 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
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@article{ubellacker_verifying_2021,\n\ttitle = {Verifying {Safe} {Transitions} between {Dynamic} {Motion} {Primitives} on {Legged} {Robots}},\n\turl = {http://arxiv.org/abs/2106.10310},\n\tabstract = {Functional autonomous systems often realize complex tasks by utilizing state machines comprised of discrete primitive behaviors and transitions between these behaviors. This architecture has been widely studied in the context of quasi-static and dynamics-independent systems. However, applications of this concept to dynamical systems are relatively sparse, despite extensive research on individual dynamic primitive behaviors, which we refer to as “motion primitives.” This paper formalizes a process to determine dynamic-state aware conditions for transitions between motion primitives in the context of safety. The result is framed as a “motion primitive graph” that can be traversed by standard graph search and planning algorithms to realize functional autonomy. To demonstrate this framework, dynamic motion primitives—including standing up, walking, and jumping—and the transitions between these behaviors are experimentally realized on a quadrupedal robot.},\n\tlanguage = {en},\n\turldate = {2021-12-14},\n\tjournal = {arXiv:2106.10310 [cs]},\n\tauthor = {Ubellacker, Wyatt and Csomay-Shanklin, Noel and Molnar, Tamas G. and Ames, Aaron D.},\n\tmonth = jun,\n\tyear = {2021},\n\tnote = {arXiv: 2106.10310},\n\tkeywords = {Computer Science - Robotics},\n}\n\n
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\n Functional autonomous systems often realize complex tasks by utilizing state machines comprised of discrete primitive behaviors and transitions between these behaviors. This architecture has been widely studied in the context of quasi-static and dynamics-independent systems. However, applications of this concept to dynamical systems are relatively sparse, despite extensive research on individual dynamic primitive behaviors, which we refer to as “motion primitives.” This paper formalizes a process to determine dynamic-state aware conditions for transitions between motion primitives in the context of safety. The result is framed as a “motion primitive graph” that can be traversed by standard graph search and planning algorithms to realize functional autonomy. To demonstrate this framework, dynamic motion primitives—including standing up, walking, and jumping—and the transitions between these behaviors are experimentally realized on a quadrupedal robot.\n
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\n \n\n \n \n \n \n \n \n Coupled Control Lyapunov Functions for Interconnected Systems, With Application to Quadrupedal Locomotion.\n \n \n \n \n\n\n \n Ma, W.; Csomay-Shanklin, N.; Kolathaya, S.; Hamed, K. A.; and Ames, A. D.\n\n\n \n\n\n\n IEEE Robotics and Automation Letters, 6(2): 3761–3768. April 2021.\n \n\n\n\n
\n\n\n\n \n \n \"CoupledPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 10 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{ma_coupled_2021,\n\ttitle = {Coupled {Control} {Lyapunov} {Functions} for {Interconnected} {Systems}, {With} {Application} to {Quadrupedal} {Locomotion}},\n\tvolume = {6},\n\tissn = {2377-3766, 2377-3774},\n\turl = {https://ieeexplore.ieee.org/document/9376602/},\n\tdoi = {10.1109/LRA.2021.3065174},\n\tabstract = {This paper addresses the problem of formally guaranteeing the stability of interconnected systems with local controllers with a view toward stabilizing quadrupeds viewed as coupled bipeds. In particular, we present a novel framework that views general rigid-body systems as a collection of lower-dimensional systems that are coupled via reaction forces. Stabilizing the corresponding coupled control system can thus be addressed by stabilizing each subsystem coupled through the passive dynamics. The main results of the paper are stability conditions that guarantee convergence for each control subsystem by formulating coupled control Lyapunov functions (CCLFs) using the notion of input-to-state stability (ISS). This theoretical result is illustrated via a simple cart-pole example, where exponential stability is obtained. Next, building on previous results where an 18-DOF quadrupedal robot is decomposed into two interconnected bipedal systems for efficient periodic gait generation, we design model-free quadratic programs (QPs) using the CCLFs to stabilize the continuous dynamics and thus achieve experimental walking and simulated hopping and running on the Vision 60 quadrupedal robot.},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2021-12-14},\n\tjournal = {IEEE Robotics and Automation Letters},\n\tauthor = {Ma, Wen-Loong and Csomay-Shanklin, Noel and Kolathaya, Shishir and Hamed, Kaveh Akbari and Ames, Aaron D.},\n\tmonth = apr,\n\tyear = {2021},\n\tpages = {3761--3768},\n}\n\n
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\n This paper addresses the problem of formally guaranteeing the stability of interconnected systems with local controllers with a view toward stabilizing quadrupeds viewed as coupled bipeds. In particular, we present a novel framework that views general rigid-body systems as a collection of lower-dimensional systems that are coupled via reaction forces. Stabilizing the corresponding coupled control system can thus be addressed by stabilizing each subsystem coupled through the passive dynamics. The main results of the paper are stability conditions that guarantee convergence for each control subsystem by formulating coupled control Lyapunov functions (CCLFs) using the notion of input-to-state stability (ISS). This theoretical result is illustrated via a simple cart-pole example, where exponential stability is obtained. Next, building on previous results where an 18-DOF quadrupedal robot is decomposed into two interconnected bipedal systems for efficient periodic gait generation, we design model-free quadratic programs (QPs) using the CCLFs to stabilize the continuous dynamics and thus achieve experimental walking and simulated hopping and running on the Vision 60 quadrupedal robot.\n
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\n  \n 2020\n \n \n (4)\n \n \n
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\n \n\n \n \n \n \n \n \n Coupled Control Systems: Periodic Orbit Generation with Application to Quadrupedal Locomotion.\n \n \n \n \n\n\n \n Ma, W.; Csomay-Shanklin, N.; and Ames, A. D.\n\n\n \n\n\n\n arXiv:2003.08507 [cs, eess, math]. March 2020.\n arXiv: 2003.08507\n\n\n\n
\n\n\n\n \n \n \"CoupledPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{ma_coupled_2020,\n\ttitle = {Coupled {Control} {Systems}: {Periodic} {Orbit} {Generation} with {Application} to {Quadrupedal} {Locomotion}},\n\tshorttitle = {Coupled {Control} {Systems}},\n\turl = {http://arxiv.org/abs/2003.08507},\n\tabstract = {A robotic system can be viewed as a collection of lower-dimensional systems that are coupled via reaction forces (Lagrange multipliers) enforcing holonomic constraints. Inspired by this viewpoint, this paper presents a novel formulation for nonlinear control systems that are subject to coupling constraints via virtual "coupling" inputs that abstractly play the role of Lagrange multipliers. The main contribution of this paper is a process---mirroring solving for Lagrange multipliers in robotic systems---wherein we isolate subsystems free of coupling constraints that provably encode the full-order dynamics of the coupled control system from which it was derived. This dimension reduction is leveraged in the formulation of a nonlinear optimization problem for the isolated subsystem that yields periodic orbits for the full-order coupled system. We consider the application of these ideas to robotic systems, which can be decomposed into subsystems. Specifically, we view a quadruped as a coupled control system consisting of two bipedal robots, wherein applying the framework developed allows for gaits (periodic orbits) to be generated for the individual biped yielding a gait for the full-order quadruped. This is demonstrated through walking experiments of a quadrupedal robot in simulation and on rough terrains.},\n\turldate = {2021-02-28},\n\tjournal = {arXiv:2003.08507 [cs, eess, math]},\n\tauthor = {Ma, Wen-Loong and Csomay-Shanklin, Noel and Ames, Aaron D.},\n\tmonth = mar,\n\tyear = {2020},\n\tnote = {arXiv: 2003.08507},\n\tkeywords = {Electrical Engineering and Systems Science - Systems and Control, Mathematics - Dynamical Systems},\n}\n\n
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\n A robotic system can be viewed as a collection of lower-dimensional systems that are coupled via reaction forces (Lagrange multipliers) enforcing holonomic constraints. Inspired by this viewpoint, this paper presents a novel formulation for nonlinear control systems that are subject to coupling constraints via virtual \"coupling\" inputs that abstractly play the role of Lagrange multipliers. The main contribution of this paper is a process—mirroring solving for Lagrange multipliers in robotic systems—wherein we isolate subsystems free of coupling constraints that provably encode the full-order dynamics of the coupled control system from which it was derived. This dimension reduction is leveraged in the formulation of a nonlinear optimization problem for the isolated subsystem that yields periodic orbits for the full-order coupled system. We consider the application of these ideas to robotic systems, which can be decomposed into subsystems. Specifically, we view a quadruped as a coupled control system consisting of two bipedal robots, wherein applying the framework developed allows for gaits (periodic orbits) to be generated for the individual biped yielding a gait for the full-order quadruped. This is demonstrated through walking experiments of a quadrupedal robot in simulation and on rough terrains.\n
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\n \n\n \n \n \n \n \n Quadrupedal Robotic Walking on Sloped Terrains Via Exact Decomposition into Coupled Bipedal Robots.\n \n \n \n\n\n \n Ma, W.; Csomay-Shanklin, N.; and Ames, A.\n\n\n \n\n\n\n ,6. 2020.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 6 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{ma_quadrupedal_2020,\n\ttitle = {Quadrupedal {Robotic} {Walking} on {Sloped} {Terrains} {Via} {Exact} {Decomposition} into {Coupled} {Bipedal} {Robots}},\n\tabstract = {Can we design motion primitives for complex legged systems uniformly for different terrain types without neglecting modeling details? This paper presents a method for rapidly generating quadrupedal locomotion on sloped terrains—from modeling to gait generation, to hardware demonstration. At the core of this approach is the observation that a quadrupedal robot can be exactly decomposed into coupled bipedal robots. Formally, this is represented through the framework of coupled control systems, wherein isolated subsystems interact through coupling constraints. We demonstrate this concept in the context of quadrupeds and use it to reduce the gait planning problem for uneven terrains to bipedal walking generation via hybrid zero dynamics. This reduction method allows for the formulation of a nonlinear optimization problem that leverages low-dimensional bipedal representations to generate dynamic walking gaits on slopes for the full-order quadrupedal robot dynamics. The result is the ability to rapidly generate quadrupedal walking gaits on a variety of slopes. We demonstrate these walking behaviors on the Vision 60 quadrupedal robot; in simulation, via walking on a range of sloped terrains of 13◦, 15◦, 20◦, 25◦, and, experimentally, through the successful locomotion of 13◦ and 20◦ ∼ 25◦ sloped outdoor grasslands.},\n\tlanguage = {en},\n\tauthor = {Ma, Wenlong and Csomay-Shanklin, Noel and Ames, Aaron},\n\tyear = {2020},\n\tpages = {6},\n}\n\n
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\n Can we design motion primitives for complex legged systems uniformly for different terrain types without neglecting modeling details? This paper presents a method for rapidly generating quadrupedal locomotion on sloped terrains—from modeling to gait generation, to hardware demonstration. At the core of this approach is the observation that a quadrupedal robot can be exactly decomposed into coupled bipedal robots. Formally, this is represented through the framework of coupled control systems, wherein isolated subsystems interact through coupling constraints. We demonstrate this concept in the context of quadrupeds and use it to reduce the gait planning problem for uneven terrains to bipedal walking generation via hybrid zero dynamics. This reduction method allows for the formulation of a nonlinear optimization problem that leverages low-dimensional bipedal representations to generate dynamic walking gaits on slopes for the full-order quadrupedal robot dynamics. The result is the ability to rapidly generate quadrupedal walking gaits on a variety of slopes. We demonstrate these walking behaviors on the Vision 60 quadrupedal robot; in simulation, via walking on a range of sloped terrains of 13◦, 15◦, 20◦, 25◦, and, experimentally, through the successful locomotion of 13◦ and 20◦ ∼ 25◦ sloped outdoor grasslands.\n
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\n \n\n \n \n \n \n \n \n Passive Dynamic Balancing and Walking in Actuated Environments.\n \n \n \n \n\n\n \n Reher, J.; Csomay-Shanklin, N.; Christensen, D. L.; Bristow, B.; Ames, A. D.; and Smoot, L.\n\n\n \n\n\n\n In 2020 IEEE International Conference on Robotics and Automation (ICRA), pages 9775–9781, Paris, France, May 2020. IEEE\n \n\n\n\n
\n\n\n\n \n \n \"PassivePaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 8 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{reher_passive_2020,\n\taddress = {Paris, France},\n\ttitle = {Passive {Dynamic} {Balancing} and {Walking} in {Actuated} {Environments}},\n\tisbn = {978-1-72817-395-5},\n\turl = {https://ieeexplore.ieee.org/document/9197400/},\n\tdoi = {10.1109/ICRA40945.2020.9197400},\n\tabstract = {The control of passive dynamic systems remains a challenging problem in the field of robotics, and insights from their study can inform everything from dynamic behaviors on actuated robots to robotic assistive devices. In this work, we explore the use of flat actuated environments for realizing passive dynamic balancing and locomotion. Specifically, we utilize a novel omnidirectional actuated floor to dynamically stabilize two robotic systems. We begin with an inverted pendulum to demonstrate the ability to control a passive system through an active environment. We then consider a passive bipedal robot wherein dynamically stable periodic walking gaits are generated through an optimization that leverages the actuated floor. The end result is the ability to demonstrate passive dynamic walking experimentally through the use of actuated environments.},\n\tlanguage = {en},\n\turldate = {2020-11-12},\n\tbooktitle = {2020 {IEEE} {International} {Conference} on {Robotics} and {Automation} ({ICRA})},\n\tpublisher = {IEEE},\n\tauthor = {Reher, Jenna and Csomay-Shanklin, Noel and Christensen, David L. and Bristow, Bobby and Ames, Aaron D. and Smoot, Lanny},\n\tmonth = may,\n\tyear = {2020},\n\tpages = {9775--9781},\n}\n\n
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\n The control of passive dynamic systems remains a challenging problem in the field of robotics, and insights from their study can inform everything from dynamic behaviors on actuated robots to robotic assistive devices. In this work, we explore the use of flat actuated environments for realizing passive dynamic balancing and locomotion. Specifically, we utilize a novel omnidirectional actuated floor to dynamically stabilize two robotic systems. We begin with an inverted pendulum to demonstrate the ability to control a passive system through an active environment. We then consider a passive bipedal robot wherein dynamically stable periodic walking gaits are generated through an optimization that leverages the actuated floor. The end result is the ability to demonstrate passive dynamic walking experimentally through the use of actuated environments.\n
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\n \n\n \n \n \n \n \n \n Automated gap-filling for marker-based biomechanical motion capture data.\n \n \n \n \n\n\n \n Camargo, J.; Ramanathan, A.; Csomay-Shanklin, N.; and Young, A.\n\n\n \n\n\n\n Computer Methods in Biomechanics and Biomedical Engineering, 0(0): 1–10. July 2020.\n Publisher: Taylor & Francis _eprint: https://doi.org/10.1080/10255842.2020.1789971\n\n\n\n
\n\n\n\n \n \n \"AutomatedPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@article{camargo_automated_2020,\n\ttitle = {Automated gap-filling for marker-based biomechanical motion capture data},\n\tvolume = {0},\n\tissn = {1025-5842},\n\turl = {https://doi.org/10.1080/10255842.2020.1789971},\n\tdoi = {10.1080/10255842.2020.1789971},\n\tabstract = {Marker-based motion capture presents the problem of gaps, which are traditionally processed using motion capture software, requiring intensive manual input. We propose and study an automated method of gap-filling that uses inverse kinematics (IK) to close the loop of an iterative process to minimize error, while nearly eliminating user input. Comparing our method to manual gap-filling, we observe a 21\\% reduction in the worst-case gap-filling error (p {\\textless} 0.05), and an 80\\% reduction in completion time (p {\\textless} 0.01). Our contribution encompasses the release of an open-source repository of the method and interaction with OpenSim.},\n\tnumber = {0},\n\turldate = {2020-09-10},\n\tjournal = {Computer Methods in Biomechanics and Biomedical Engineering},\n\tauthor = {Camargo, Jonathan and Ramanathan, Aditya and Csomay-Shanklin, Noel and Young, Aaron},\n\tmonth = jul,\n\tyear = {2020},\n\tpmid = {32654510},\n\tnote = {Publisher: Taylor \\& Francis\n\\_eprint: https://doi.org/10.1080/10255842.2020.1789971},\n\tkeywords = {Motion capture, biomechanics, gap-filling, inverse kinematics},\n\tpages = {1--10},\n}\n\n
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\n Marker-based motion capture presents the problem of gaps, which are traditionally processed using motion capture software, requiring intensive manual input. We propose and study an automated method of gap-filling that uses inverse kinematics (IK) to close the loop of an iterative process to minimize error, while nearly eliminating user input. Comparing our method to manual gap-filling, we observe a 21% reduction in the worst-case gap-filling error (p \\textless 0.05), and an 80% reduction in completion time (p \\textless 0.01). Our contribution encompasses the release of an open-source repository of the method and interaction with OpenSim.\n
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\n \n\n \n \n \n \n \n Design and Comparative Analysis of 1D Hopping Robots.\n \n \n \n\n\n \n Ambrose, E.; Csomay-Shanklin, N.; Or, Y.; and Ames, A.\n\n\n \n\n\n\n In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 5717–5724, November 2019. \n ISSN: 2153-0858\n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{ambrose_design_2019,\n\ttitle = {Design and {Comparative} {Analysis} of {1D} {Hopping} {Robots}},\n\tdoi = {10.1109/IROS40897.2019.8967692},\n\tabstract = {Hopping is a highly dynamic motion requiring precise input over brief moments of ground contact in order to achieve desired performance. While this problem has been approached from multiple perspectives, this work provides a comparative analysis of two robot models. The first model uses an actuator to store energy in a spring and release it during the ground phase, while the second uses an actuator to move an additional mass vertically to generate force on the spring. In the first model, analytic expressions are used to find the desired controllers, while trajectory optimization is used in the latter. Orbital stability of each model under the conditions of uncertain damping and poor estimation of the hop height is examined. To this end, Poincaré analysis is used to give a metric of stability in the presence of different initial conditions and parameter uncertainty. Simulations show that the first model converges quickly to a point near the desired height determined by the amount of uncertain damping present. The second model is less robust to uncertainty, but is be made to converge to a desired height with the addition of PD control around the optimal trajectory. This robustness is improved with different gains in the controller. In experiments performed on hardware for the second model, stability is observed through convergence to a periodic orbit within several hops.},\n\tbooktitle = {2019 {IEEE}/{RSJ} {International} {Conference} on {Intelligent} {Robots} and {Systems} ({IROS})},\n\tauthor = {Ambrose, Eric and Csomay-Shanklin, Noel and Or, Yizhar and Ames, Aaron},\n\tmonth = nov,\n\tyear = {2019},\n\tnote = {ISSN: 2153-0858},\n\tpages = {5717--5724},\n}\n\n
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\n Hopping is a highly dynamic motion requiring precise input over brief moments of ground contact in order to achieve desired performance. While this problem has been approached from multiple perspectives, this work provides a comparative analysis of two robot models. The first model uses an actuator to store energy in a spring and release it during the ground phase, while the second uses an actuator to move an additional mass vertically to generate force on the spring. In the first model, analytic expressions are used to find the desired controllers, while trajectory optimization is used in the latter. Orbital stability of each model under the conditions of uncertain damping and poor estimation of the hop height is examined. To this end, Poincaré analysis is used to give a metric of stability in the presence of different initial conditions and parameter uncertainty. Simulations show that the first model converges quickly to a point near the desired height determined by the amount of uncertain damping present. The second model is less robust to uncertainty, but is be made to converge to a desired height with the addition of PD control around the optimal trajectory. This robustness is improved with different gains in the controller. In experiments performed on hardware for the second model, stability is observed through convergence to a periodic orbit within several hops.\n
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\n \n\n \n \n \n \n \n Sampling Tool Concepts for Enceladus Lander In-Situ Analysis.\n \n \n \n\n\n \n Badescu, M.; Riccobono, D.; Ubellacker, S.; Backes, P.; Dotson, M.; Molaro, J.; Moreland, S.; Csomay-Shanklin, N.; Choukroun, M.; Brinkman, A.; and Genta, G.\n\n\n \n\n\n\n In 2019 IEEE Aerospace Conference, pages 1–12, March 2019. \n ISSN: 1095-323X\n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@inproceedings{badescu_sampling_2019,\n\ttitle = {Sampling {Tool} {Concepts} for {Enceladus} {Lander} {In}-{Situ} {Analysis}},\n\tdoi = {10.1109/AERO.2019.8741568},\n\tabstract = {A potential future in-situ lander mission to the surface of Enceladus could be the lowest cost mission to determine if life exists beyond Earth since material from the subsurface ocean, where the presence of hydrothermal activity has been strongly suggested by the Cassini mission, is available on its surface after being ejected by plumes and then settling on the surface. In addition, the low radiation environment of Enceladus would not significantly alter the chemical makeup of samples recently deposited on the surface. A study was conducted to explore various sampling devices that could be used by an in-situ lander mission to provide 1cc to 5cc volume samples to instruments. In addition to temperature and vacuum environmental conditions, the low surface gravity of Enceladus (1\\% of Earth gravity)represents a new challenge for surface sampling that is not met by sampling systems developed for microgravity (e.g., comets and asteroids)or higher gravity (e.g., Europa 13\\%g, Moon 16\\%g, or Mars 38\\%g)environments. It is desired to acquire surface plume material that has accumulated in the top 1cm to ensure acquisition of the least processed material. Several sampling devices were developed or adapted and then tested in simulated conditions that resemble the Enceladus surface properties. These devices and test results are presented in this paper.},\n\tbooktitle = {2019 {IEEE} {Aerospace} {Conference}},\n\tauthor = {Badescu, Mircea and Riccobono, Dario and Ubellacker, Samuel and Backes, Paul and Dotson, Matthew and Molaro, Jamie and Moreland, Scott and Csomay-Shanklin, Noel and Choukroun, Mathieu and Brinkman, Alexander and Genta, Giancarlo},\n\tmonth = mar,\n\tyear = {2019},\n\tnote = {ISSN: 1095-323X},\n\tkeywords = {5cc volume samples, Cassini mission, Earth gravity, Enceladus lander, Enceladus surface properties, Gravity, In, Mars, Ocean temperature, Propulsion, Saturn, Sea surface, Surface treatment, Tools, asteroids, higher gravity, hydrothermal activity, low radiation environment, low surface gravity, lowest cost mission, planetary atmospheres, planetary satellites, planetary surfaces, potential future in-situ lander mission, processed material, sampling devices, sampling systems, sampling tool, seafloor phenomena, size 1.0 cm, space vehicles, subsurface ocean, surface plume material, surface sampling, temperature, vacuum environmental conditions},\n\tpages = {1--12},\n}\n
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\n A potential future in-situ lander mission to the surface of Enceladus could be the lowest cost mission to determine if life exists beyond Earth since material from the subsurface ocean, where the presence of hydrothermal activity has been strongly suggested by the Cassini mission, is available on its surface after being ejected by plumes and then settling on the surface. In addition, the low radiation environment of Enceladus would not significantly alter the chemical makeup of samples recently deposited on the surface. A study was conducted to explore various sampling devices that could be used by an in-situ lander mission to provide 1cc to 5cc volume samples to instruments. In addition to temperature and vacuum environmental conditions, the low surface gravity of Enceladus (1% of Earth gravity)represents a new challenge for surface sampling that is not met by sampling systems developed for microgravity (e.g., comets and asteroids)or higher gravity (e.g., Europa 13%g, Moon 16%g, or Mars 38%g)environments. It is desired to acquire surface plume material that has accumulated in the top 1cm to ensure acquisition of the least processed material. Several sampling devices were developed or adapted and then tested in simulated conditions that resemble the Enceladus surface properties. These devices and test results are presented in this paper.\n
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