@article {1414694,
	title = {Effective Locomotion at Multiple Stride Frequencies Using Proprioceptive Feedback on a Legged Microrobot},
	journal = {Bioinspiration \& Biomimetics},
	year = {In Press},
	author = {Neel Doshi and Kaushik Jayaram and Samantha Castellanos and Scott Kuindersma and Wood, Robert J.}
}
@article {1413826,
	title = {A New Control Framework for Flapping-Wing Vehicles Based on 3D Pendulum Dynamics},
	journal = {Automatica},
	year = {In Press},
	abstract = {
	In this paper, a new control framework for an insect-scale flapping-wing vehicle is presented that exploits passive aerodynamic effects to stabilize the attitude dynamics. Many flapping-wing robotic flyers and flying insects share a common morphological feature in that the center of mass (CoM) is below the center of pressure (CoP), which makes the hovering configuration intrinsically unstable with open-loop control. Motivated by the fact that the CoM should be ahead of the CoP to ensure the longitudinal stability of the flight dynamics, a new coordinate system is proposed by placing a virtual control point (VCP) above the CoP. The dynamics in the new coordinates are derived using a near-identity diffeomorphism which admits a partial feedback linearization with stable zero dynamics. The behavior of the zero dynamics resembles the dynamics of a 3D pendulum with an aerodynamic damper. An adaptive controller is proposed to make the upright orientation almost globally asymptotically stable over a bounded uncertainty of the aerodynamic drag coefficient. The controller is evaluated in simulation with a Harvard RoboBee following a virtual control point reference trajectory.

},
	author = {Nak-seung P. Hyun and Rebecca McGill and Wood, Robert J. and Scott Kuindersma}
}
@proceedings {1431741,
	title = {Bayesian Optimization of Soft Exosuits Using a Metabolic Estimator Stopping Process },
	journal = {International Conference on Robotics and Automation (ICRA)},
	year = {2019},
	abstract = {Recent human-in-the-loop (HIL) optimization studies using wearable devices have shown an improved average metabolic reduction by optimizing a small number of control parameters during short-duration walking experiments. However, the slow metabolic dynamics, high measurement noise, and experimental time constraints create challenges for increasing the number of control parameters to be optimized. Prior work applying gradient descent and Bayesian optimization to this problem have decoupled metabolic estimation and control parameter selection using fixed estimation intervals, which imposes a hard limit on the number of parameter evaluations possible in a given time budget. In this work, we take a different approach that couples estimation and parameter selection, allowing the algorithm to spend less time on refining the metabolic estimates for parameters that are unlikely to improve performance over the best observed values. Our approach uses a Kalman filter-based metabolic estimator to formulate an optimal stopping problem during the data acquisition step of standard Bayesian optimization. Performance was analyzed in numerical simulations and in pilot human subject testing with two subjects that involved optimizing six control parameters of a single-joint exosuit and four parameters of a multi-joint exosuit.},
	author = {Kim, Myunghee and Charles Liu and Jinsoo Kim and Lee, Sangjun and Adham Meguid and Walsh, Conor J. and Scott Kuindersma}
}
@article {1314256,
	title = {Contact-Implicit Trajectory Optimization using Variational Integrators},
	journal = {International Journal of Robotics Research},
	year = {2019},
	note = {This paper is currently under review. Suggestions and comments welcome!\&nbsp;},
	abstract = {Contact constraints arise naturally in many robot planning problems. In recent years, a variety of contact-implicit trajectory optimization algorithms have been developed that avoid the pitfalls of mode pre-specification by simultaneously optimizing state, input, and contact force trajectories. However, their reliance on first-order integrators leads to a linear tradeoff between optimization problem size and plan accuracy. To address this limitation, we propose a new family of trajectory optimization algorithms that leverage ideas from discrete variational mechanics to derive higher-order generalizations of the classic time-stepping method of Stewart and Trinkle. By using these dynamics formulations as constraints in direct trajectory optimization algorithms, it is possible to perform contact-implicit trajectory optimization with significantly higher accuracy. For concreteness, we derive a second-order method and evaluate it using several simulated rigid body systems, including an underactuated biped and a quadruped. In addition, we use this second-order method to plan locomotion trajectories for a complex quadrupedal microrobot. The planned trajectories are evaluated on the physical platform and result in a number of performance improvements.},
	url = {https://journals.sagepub.com/doi/abs/10.1177/0278364919849235?journalCode=ijra},
	author = {Zachary Manchester and Neel Doshi and Wood, Robert J. and Scott Kuindersma}
}
@conference {1322086,
	title = {A Performance Analysis of Parallel Differential Dynamic Programming on a GPU},
	booktitle = {International Workshop on the Algorithmic Foundations of Robotics (WAFR)},
	year = {2018},
	abstract = {Parallelism can be used to significantly increase the throughput of computationally expensive algorithms. With the widespread adoption of parallel computing platforms such as GPUs, it is natural to consider whether these architectures can benefit robotics researchers interested in solving trajectory optimization problems online. Differential Dynamic Programming (DDP) algorithms have been shown to achieve some of the best timing performance in robotics tasks by making use of optimized dynamics methods and CPU multi-threading. This paper aims to analyze the benefits and tradeoffs of higher degrees of parallelization using a multiple-shooting variant of DDP implemented on a GPU. We describe our implementation strategy and present results demonstrating its performance compared to an equivalent multi-threaded CPU implementation using several benchmark control tasks. Our results suggest that GPU-based solvers can offer increased per-iteration computation time and faster convergence in some cases, but in general tradeoffs exist between convergence behavior and degree of parallelism.},
	author = {Brian Plancher and Scott Kuindersma}
}
@conference {1322085,
	title = {A Constrained Kalman Filter for Rigid Body Systems with Frictional Contact},
	booktitle = {International Workshop on the Algorithmic Foundations of Robotics (WAFR)},
	year = {2018},
	abstract = {Contact interactions are central to robot manipulation and locomotion behaviors. State estimation techniques that explicitly capture the dynamics of contact offer the potential to reduce estimation errors from unplanned contact events and improve closed-loop control performance. This is particularly true in highly dynamic situations where common simplifications like no-slip or quasi-static sliding are violated. Incorporating contact constraints requires care to address the numerical challenges associated with discontinuous dynamics, which make straightforward application of derivative-based techniques such as the Extended Kalman Filter impossible. In this paper, we derive an approximate maximum a posteriori estimator that can handle rigid body contact by explicitly imposing contact constraints in the observation update. We compare the performance of this estimator to an existing state-of-the-art Unscented Kalman Filter designed for estimation through contact and demonstrate the scalability of the approach by estimating the state of a 20-DOF bipedal robot in realtime.},
	author = {Patrick Varin and Scott Kuindersma}
}
@conference {1308158,
	title = {Contact-Implicit Optimization of Locomotion Trajectories for a Quadrupedal Microrobot},
	booktitle = {Robotics: Science and Systems (RSS)},
	year = {2018},
	note = {},
	abstract = {Planning locomotion trajectories for legged microrobots is challenging. This is because of their complex morphology, high frequency passive dynamics, and discontinuous contact interactions with their environment. Consequently, such research is often driven by time-consuming experimental methods. As an alternative, we present a framework for systematically modeling, planning, and controlling legged microrobots. We develop a three- dimensional dynamic model of a 1.5 g quadrupedal microrobot with complexity (e.g., number of degrees of freedom) similar to larger-scale legged robots. We then adapt a recently developed variational contact-implicit trajectory optimization method to generate feasible whole-body locomotion plans for this microrobot, and demonstrate that these plans can be tracked with simple joint-space controllers. We plan and execute periodic gaits at multiple stride frequencies and on various surfaces. These gaits achieve high per-cycle velocities, including a maximum of 10.87 mm/cycle, which is 15\% faster than previously measured for this microrobot. Furthermore, we plan and execute a vertical jump of 9.96 mm, which is 78\% of the microrobot{\textquoteright}s center-of- mass height. To the best of our knowledge, this is the first end-to-end demonstration of planning and tracking whole-body dynamic locomotion on a millimeter-scale legged microrobot.},
	author = {Neel Doshi and Kaushik Jayaram and Benjamin Goldberg and Zachary Manchester and Wood, Robert J. and Scott Kuindersma}
}
@article {1302117,
	title = {Human-in-the-loop optimization of hip assistance with a soft exosuit during walking},
	journal = {Science Robotics},
	volume = {3},
	number = {15},
	year = {2018},
	note = {},
	pages = {eaar5438},
	abstract = {Wearable robotic devices have been shown to substantially reduce the energy expenditure of human walking. However, response variance between participants for fixed control strategies can be high, leading to the hypothesis that individualized controllers could further improve walking economy. Recent studies on human-in-the-loop (HIL) control optimization have elucidated several practical challenges, such as long experimental protocols and low signal-to-noise ratios. Here, we used Bayesian optimization{\textemdash}an algorithm well suited to optimizing noisy performance signals with very limited data{\textemdash}to identify the peak and offset timing of hip extension assistance that minimizes the energy expenditure of walking with a textile-based wearable device. Optimal peak and offset timing were found over an average of 21.4 {\textpm} 1.0 min and reduced metabolic cost by 17.4 {\textpm} 3.2\% compared with walking without the device (mean {\textpm} SEM), which represents an improvement of more than 60\% on metabolic reduction compared with state-of-the-art devices that only assist hip extension. In addition, our results provide evidence for participant-specific metabolic distributions with respect to peak and offset timing and metabolic landscapes, lending support to the hypothesis that individualized control strategies can offer substantial benefits over fixed control strategies. These results also suggest that this method could have practical impact on improving the performance of wearable robotic devices.},
	url = {http://robotics.sciencemag.org/content/3/15/eaar5438},
	author = {Ye Ding and Kim, Myunghee and Scott Kuindersma and Walsh, Conor J.}
}
@article {1283701,
	title = {Robust Direct Trajectory Optimization Using Approximate Invariant Funnels},
	journal = {Autonomous Robots},
	volume = {43},
	number = {2},
	year = {2018},
	pages = {375-387},
	abstract = {Many critical robotics applications require robustness to disturbances arising from unplanned forces, state uncertainty, and model errors. Motion planning algorithms that explicitly reason about robustness require a coupling of trajectory optimization and feedback design, where the system{\textquoteright}s closed-loop response to disturbances is optimized. Due to the often-heavy computational demands of solving such problems, the practical application of robust trajectory optimization in robotics has so far been limited. Motivated by recent work on sums-of-squares verification methods for nonlinear systems, we derive a scalable robust trajectory optimization algorithm that optimizes approximate invariant funnels along the trajectory while planning. For the case of ellipsoidal disturbance sets and LQR feedback controllers, the state and input deviations along a nominal trajectory can be computed locally in closed form, permitting fast evaluation of robust cost and constraint functions and their derivatives. The resulting algorithm is a scalable extension of classical direct transcription that demonstrably improves tracking performance over non-robust formulations while incurring only a modest increase in computational cost. We evaluate the algorithm in several simulated robot control tasks.},
	url = {http://em.rdcu.be/wf/click?upn=lMZy1lernSJ7apc5DgYM8e24xrYhSFK5X6cE3-2BZib5M-3D_Me-2F-2BXTdGod-2FpQrhDtHpJFnHIppVTOp0teMLWFtYuVbLH9VkVs6ZHPeOVCZtrPQefHbFmWSDdMZ4k7EiE9-2F20-2FmhTyIJEHaAGPPECVx6ytpZ6rtS4Uhd9AUtjPie4uLbSHsoE185-2B9jap0vKqAicYw33lti714dqsrYx},
	author = {Zachary Manchester and Scott Kuindersma}
}
@article {1178336,
	title = {Human-in-the-loop Bayesian optimization of wearable device parameters},
	journal = {PLoS ONE},
	volume = {12},
	number = {9},
	year = {2017},
	pages = {e0184054},
	abstract = {
	The increasing capabilities of exoskeletons and powered prosthetics for walking assistance have paved the way for more sophisticated and individualized control strategies. In response to this opportunity, recent work on\&nbsp;human-in-the-loop\&nbsp;optimization has considered the problem of automatically tuning control parameters based on realtime physiological measurements. However, the common use of metabolic cost as a performance metric creates significant experimental challenges due to its long measurement times and low signal-to-noise ratio. We evaluate the use of Bayesian optimization{\textemdash}a family of sample-efficient, noise-tolerant, and global optimization methods{\textemdash}for quickly identifying near-optimal control parameters. To manage experimental complexity and provide comparisons against related work, we consider the task of minimizing metabolic cost by optimizing walking step frequencies in unaided human subjects. Compared to an existing approach based on gradient descent, Bayesian optimization identified a near-optimal step frequency with a faster time to convergence (12 minutes,\&nbsp;p\&nbsp;\&lt; 0.01), smaller inter-subject variability in convergence time ({\textpm} 2 minutes,\&nbsp;p\&nbsp;\&lt; 0.01), and lower overall energy expenditure (p\&nbsp;\&lt; 0.01).

},
	url = {http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0184054},
	author = {Kim, Myunghee and Ye Ding and Philippe Malcolm and Jozefien Speeckaert and Siviy, Christoper J. and Walsh, Conor J. and Scott Kuindersma}
}
@conference {1162566,
	title = {Variational Contact-Implicit Trajectory Optimization},
	booktitle = {International Symposium on Robotics Research (ISRR)},
	year = {2017},
	address = {Puerto Varas, Chile},
	abstract = {Contact constraints arise naturally in many robot planning problems. In recent years, a variety of contact-implicit trajectory optimization algorithms have been developed that avoid the pitfalls of mode pre-specification by simultaneously optimizing state, input, and contact force trajectories. However, their reliance on first-order integrators leads to a linear tradeoff between optimization problem size and plan accuracy. To address this limitation, we propose a new family of trajectory optimization algorithms that leverage ideas from discrete variational mechanics to derive higher-order generalizations of the classic time-stepping method of Stewart and Trinkle. By using these dynamics formulations as constraints in direct trajectory optimization algorithms, it is possible to perform contact-implicit trajectory optimization with significantly higher accuracy. For concreteness, we derive a second-order method and evaluate it using several simulated rigid body systems including an underactuated biped and a quadruped.},
	author = {Zachary Manchester and Scott Kuindersma}
}
@conference {1093301,
	title = {Constrained Unscented Dynamic Programming},
	booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
	year = {2017},
	abstract = {Differential Dynamic Programming (DDP) has become a popular approach to performing trajectory optimization for complex, underactuated robots. However, DDP presents two practical challenges. First, the evaluation of dynamics derivatives during optimization creates a computational bottleneck, particularly in implementations that capture second-order dynamic effects. Second, constraints on the states (e.g., boundary conditions, collision constraints, etc.) require additional care since the state trajectory is implicitly defined from the inputs and dynamics.\&nbsp;This paper addresses both of these problems by building on recent work on Unscented Dynamic Programming (UDP)---which eliminates dynamics derivative computations in DDP---to support general nonlinear state and input constraints using an augmented Lagrangian. The resulting algorithm has the same computational cost as first-order penalty-based DDP variants, but can achieve high-accuracy constraint satisfaction without the numerical ill-conditioning associated with penalty methods. We present results demonstrating its favorable performance on several simulated dynamical systems including a quadrotor and 7-DoF robot arm.},
	author = {Brian Plancher and Zachary Manchester and Scott Kuindersma}
}
@conference {1061961,
	title = {DIRTREL: Robust Trajectory Optimization with Ellipsoidal Disturbances and LQR Feedback},
	booktitle = {Robotics: Science and Systems (RSS)},
	year = {2017},
	note = {Code:\&nbsp;https://github.com/HarvardAgileRoboticsLab/drake/tree/dirtrel},
	abstract = {Many critical robotics applications require robustness to disturbances arising from unplanned forces, state uncertainty, and model errors. Motion planning algorithms that explicitly reason about robustness require a coupling of trajectory optimization and feedback design, where the system{\textquoteright}s closed-loop response to bounded disturbances is optimized. Due to the often-heavy computational demands of solving such problems, the practical application of robust trajectory optimization in robotics has so far been limited. We derive a tractable robust optimization algorithm that combines direct transcription with linear-quadratic control design to reason about closed-loop responses to disturbances. In the case of ellipsoidal disturbance sets, the state and input deviations along a nominal trajectory can be computed locally in closed form, thus allowing for fast evaluations of robust cost and constraint functions. The resulting algorithm, called DIRTREL, is an extension of classical direct transcription that demonstrably improves tracking performance over non-robust formulations while incurring only a modest increase in computational cost. We evaluate the algorithm in several simulated robot control tasks.},
	author = {Zachary Manchester and Scott Kuindersma}
}
@conference {726726,
	title = {A Variable Forward-Sweep Wing Design for Improved Perching in Micro Aerial Vehicles},
	booktitle = {55th AIAA Aerospace Sciences Meeting, AIAA SciTech Forum},
	year = {2017},
	abstract = {A micro aerial vehicle with a variable forward-sweep wing is proposed with the goal of enhancing performance and controllability during high-angle-of-attack perching maneuvers. Data is presented from a series of wind tunnel experiments to quantify the aerodynamic effects of forward sweep over a range of angles of attack from -25 degrees\&nbsp;to +75\&nbsp;degrees. A nonlinear dynamics model is constructed using the wind tunnel data to gain further insight into aircraft flight dynamics and controllability. Simulated perching trajectories optimized with a direct collocation method indicate that the forward-swept wing configuration can achieve qualitatively different lower-cost perching maneuvers than the straight wing configuration.},
	author = {Zachary R. Manchester and Jeffrey I. Lipton and Wood, Robert J. and Scott Kuindersma}
}
@conference {Manchester16,
	title = {Derivative-Free Trajectory Optimization with Unscented Dynamic Programming},
	booktitle = {Proceedings of the 55th Conference on Decision and Control (CDC)},
	year = {2016},
	abstract = {Trajectory optimization algorithms are a core technology behind many modern nonlinear control applications. However, with increasing system complexity, the computation of dynamics derivatives during optimization creates a computational bottleneck, particularly in second-order methods. In this paper, we present a modification of the classical Differential Dynamic Programming (DDP) algorithm that eliminates the computation of dynamics derivatives while maintaining similar convergence properties. Rather than relying on naive finite difference calculations, we propose a deterministic sampling scheme inspired by the Unscented Kalman Filter that propagates a quadratic approximation of the cost-to-go function through the nonlinear dynamics at each time step. Our algorithm takes larger steps than Iterative LQR---a DDP variant that approximates the cost-to-go Hessian using only first derivatives---while maintaining the same computational cost. We present results demonstrating its numerical performance in simulated balancing and aerobatic flight experiments.\&nbsp;\&nbsp;Code:\&nbsp;https://github.com/HarvardAgileRoboticsLab/unscented-dynamic-programming},
	author = {Zachary Manchester and Scott Kuindersma}
}
@article {Marion16,
	title = {Director: A User Interface Designed for Robot Operation with Shared Autonomy},
	journal = {Journal of Field Robotics},
	year = {2016},
	note = {[[{"fid":"3193401","view_mode":"default","type":"media","attributes":{"height":"360","width":"480","alt":"MIT Atlas - Visualization in DRC Finals (1st Run - Friday) on YouTube","title":"MIT Atlas - Visualization in DRC Finals (1st Run - Friday)","class":"media-element file-default"}}]]},
	month = {2016},
	abstract = {Operating a high degree of freedom mobile manipulator, such as a humanoid, in a field scenario requires constant situational awareness, capable perception modules, and effective mechanisms for interactive motion planning and control. A well-designed operator interfacepresents the operator with enough context to quickly carry out a mission and the flexibility to handle unforeseen operating scenarios robustly. By contrast, an unintuitive user interface can increase the risk of catastrophic operator error by overwhelming the user with unnecessary information. With these principles in mind, we present the philosophy and design decisions behind Director---the open-source user interface developed by Team MIT to pilot the Atlas robot in the DARPA Robotics Challenge (DRC). At the heart of Director is an integrated task execution system that specifies sequences of actions needed to achieve a substantive task, such as drilling a wall or climbing a staircase. These task sequences, developed a priori, make online queries to automated perception and planning algorithms with outputs that can be reviewed by the operator and executed by our whole-body controller. Our use of Director at the DRC resulted in efficient high-level task operation while being fully competitive with approaches focusing on teleoperation by highly-trained operators. We discuss the primary interface elements that comprise the Director and provide analysis of its successful use at the DRC.\&nbsp;Publisher{\textquoteright}s link:\&nbsp;http://onlinelibrary.wiley.com/doi/10.1002/rob.21681/full},
	author = {Marion, Pat and Fallon, Maurice and Deits, Robin and Valenzuela, Andr{\'e}s and Perez-D{\textquoteright}Arpino, Claudia and Greg Izatt and Lucas Manuelli and Antone, Matthew and Dai, Hongkai and Koolen, Twan and John Carter and Scott Kuindersma and Russ Tedrake}
}
@inbook {Wieber16,
	title = {Modeling and Control of Legged Systems},
	booktitle = {Springer Handbook of Robotics, 2nd Ed},
	year = {2016},
	publisher = {Springer},
	organization = {Springer},
	abstract = {The promise of legged robots over standard wheeled robots is to provide\&nbsp;improved mobility over rough terrain. This promise builds on the\&nbsp;decoupling between the environment and the main body of the robot that\&nbsp;the presence of articulated legs allows, with two consequences. First,\&nbsp;the motion of the main body of the robot can be made largely independent\&nbsp;from the roughness of the terrain, within the kinematic limits of the\&nbsp;legs: legs provide an active suspension system. Indeed, one of the most advanced\&nbsp;hexapod robots of the 1980s was aptly called the Adaptive Suspension\&nbsp;Vehicle. Second, this decoupling allows\&nbsp;legs to temporarily leave their contact with the ground: isolated\&nbsp;footholds on a discontinuous terrain can be overcome, allowing to visit\&nbsp;places absolutely out of reach otherwise. Note that having feet firmly\&nbsp;planted on the ground is not mandatory here: skating is an equally\&nbsp;interesting option, although rarely approached so far in robotics.
Unfortunately, this promise comes at the cost of a hindering increase in\&nbsp;complexity. It is only with the unveiling of the Honda P2 humanoid robot\&nbsp;in 1996, and later of the Boston Dynamics BigDog\&nbsp;quadruped robot in 2005 that legged robots finally began to deliver\&nbsp;real-life capacities that are just beginning to match the long sought\&nbsp;animal-like mobility over rough terrain. In fact, work in legged\&nbsp;robotics has even contributed to the understanding of human and animal\&nbsp;locomotion, as evidenced by the many fruitful collaborations between\&nbsp;robotics and biomechanics researchers over legged locomotion.},
	author = {Pierre-Brice Wieber and Russ Tedrake and Scott Kuindersma},
	editor = {Siciliano, Bruno and Khatib, Oussama}
}
@conference {Posa16,
	title = {Optimization and stabilization of trajectories for constrained dynamical systems},
	booktitle = {Proceedings of the International Conference on Robotics and Automation (ICRA)},
	year = {2016},
	note = {[[{"fid":"3193341","view_mode":"default","type":"media","attributes":{"height":"360","width":"480","alt":"ICRA 2016 video on YouTube","title":"ICRA 2016 video","class":"media-element file-default"}}]]},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Stockholm, Sweden},
	abstract = {Contact constraints, such as those between a foot and the ground or a hand and an object, are inherent in many robotic tasks. These constraints define a manifold of feasible states; while well understood mathematically, they pose numerical challenges to many algorithms for planning and controlling whole-body dynamic motions. In this paper, we present an approach to the synthesis and stabilization of complex trajectories for both fully-actuated and underactuated robots subject to contact constraints. We introduce a trajectory optimization algorithm (DIRCON) that extends the direct collocation method, naturally incorporating manifold constraints to produce a nominal trajectory with third-order integration accuracy{\textendash}-a critical feature for achieving reliable tracking control. We adapt the classical time-varying linear quadratic regulator to produce a local cost-to-go in the manifold tangent plane. Finally, we descend the cost-to-go using a quadratic program that incorporates unilateral friction and torque constraints. This approach is demonstrated on three complex walking and climbing locomotion examples in simulation.},
	author = {Posa, Michael and Scott Kuindersma and Russ Tedrake}
}
@article {Kuindersma16,
	title = {Optimization-based locomotion planning, estimation, and control design for Atlas},
	journal = {Autonomous Robots},
	volume = {40},
	number = {3},
	year = {2016},
	note = {
	Winner of the IEEE-RAS Technical Commmittee on Whole-Body Control 2016 Best Paper of the Year Award



	\&nbsp;



	[[{"fid":"3193196","view_mode":"default","type":"media","attributes":{"height":"360","width":"480","alt":"Egress Robustness Tests on YouTube","title":"Egress Robustness Tests","class":"media-element file-default"}}]]



	[[{"fid":"3193216","view_mode":"default","type":"media","attributes":{"height":"360","width":"480","alt":"Atlas walking, running, and jumping in simulation on YouTube","title":"Atlas walking, running, and jumping in simulation","class":"media-element file-default"}}]]

},
	pages = {429{\textendash}455},
	abstract = {
	This paper describes a collection of optimization algorithms for achieving dynamic planning, control, and state estimation for a bipedal robot designed to operate reliably in complex environments. To make challenging locomotion tasks tractable, we describe several novel applications of convex, mixed-integer, and sparse nonlinear optimization to problems ranging from footstep placement to whole-body planning and control. We also present a state estimator formulation that, when combined with our walking controller, permits highly precise execution of extended walking plans over non-flat terrain. We describe our complete system integration and experiments carried out on Atlas, a full-size hydraulic humanoid robot built by Boston Dynamics, Inc.

},
	doi = {10.1007/s10514-015-9479-3},
	author = {Scott Kuindersma and Deits, Robin and Fallon, Maurice and Valenzuela, Andr{\'e}s and Dai, Hongkai and Frank Permenter and Koolen, Twan and Marion, Pat and Russ Tedrake}
}
@article {Fallon15,
	title = {An Architecture for Online Affordance-based Perception and Whole-body Planning},
	journal = {Journal of Field Robotics},
	volume = {32},
	number = {2},
	year = {2015},
	note = {[[{"fid":"3193166","view_mode":"default","type":"media","attributes":{"height":"360","width":"480","alt":"Summary Video - Team MIT in the DRC Trials on YouTube","title":"Summary Video - Team MIT in the DRC Trials","class":"media-element file-default"}}]]},
	pages = {229{\textendash}254},
	abstract = {The DARPA Robotics Challenge Trials held in December 2013 provided a landmark demonstration of dexterous mobile robots executing a variety of tasks aided by a remote human operator using only data from the robot{\textquoteright}s sensor suite transmitted over a constrained, field-realistic communications link. We describe the design considerations, architecture, implementation, and performance of the software that Team MIT developed to command and control an Atlas humanoid robot. Our design emphasized human interaction with an efficient motion planner, where operators expressed desired robot actions in terms of affordances fit using perception and manipulated in a custom user interface. We highlight several important lessons we learned while developing our system on a highly compressed schedule.},
	doi = {10.1002/rob.21546},
	author = {Fallon, Maurice and Scott Kuindersma and Karumanchi, Sisir and Antone, Matthew and Schneider, Toby and Dai, Hongkai and P{\'e}rez D{\textquoteright}Arpino, Claudia and Deits, Robin and DiCicco, Matt and Fourie, Dehann and Koolen, Twan and Marion, Pat and Posa, Michael and Valenzuela, Andr{\'e}s and Yu, Kuan-Ting and Shah, Julie and Iagnemma, Karl and Russ Tedrake and Teller, Seth}
}
@conference {Tedrake15,
	title = {A closed-form solution for real-time ZMP gait generation and feedback stabilization},
	booktitle = {IEEE-RAS International Conference on Humanoid Robots},
	year = {2015},
	address = {Seoul, Korea},
	abstract = {Here we present a closed-form solution to the continuous time-varying linear quadratic regulator (LQR) problem for the zero-moment point (ZMP) tracking controller. This generalizes previous analytical solutions for gait generation by allowing {\textquoteleft}{\textquoteleft}soft" tracking (with a quadratic cost) of the desired ZMP, and by providing the feedback gains for the resulting time-varying optimal controller. This enables extremely fast computation, with the number of operations linear in the number of spline segments representing the desired ZMP. Results are presented using the Atlas humanoid robot where dynamic walking is achieved by recomputing the optimal controller online.},
	author = {Russ Tedrake and Scott Kuindersma and Deits, Robin and Kanako Miura}
}
@conference {Kuindersma14,
	title = {An Efficiently Solvable Quadratic Program for Stabilizing Dynamic Locomotion},
	booktitle = {Proceedings of the International Conference on Robotics and Automation (ICRA)},
	year = {2014},
	note = {[[{"fid":"3193276","view_mode":"default","type":"media","attributes":{"height":"360","width":"480","alt":"Atlas walking in Gazebo simulation on YouTube","title":"Atlas walking in Gazebo simulation","class":"media-element file-default"}}]]
[[{"fid":3193216,"view_mode":"default","type":"media","attributes":{"height":"360","width":"480","alt":"Atlas walking, running, and jumping in simulation on YouTube","title":"Atlas walking, running, and jumping in simulation","class":"media-element file-default"}}]]},
	pages = {2589{\textendash}2594},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Hong Kong, China},
	abstract = {We describe a whole-body dynamic walking controller implemented as a convex quadratic program. The controller solves an optimal control problem using an approximate value function derived from a simple walking model while respecting the dynamic, input, and contact constraints of the full robot dynamics. By exploiting sparsity and temporal structure in the optimization with a custom active-set algorithm, we surpass the performance of the best available off-the-shelf solvers and achieve 1kHz control rates for a 34-DOF humanoid. We describe applications to balancing and walking tasks using the simulated Atlas robot in the DARPA Virtual Robotics Challenge.},
	isbn = {978-1-4799-3685-4},
	doi = {10.1109/ICRA.2014.6907230},
	author = {Scott Kuindersma and Frank Permenter and Russ Tedrake}
}
@conference {Tedrake14a,
	title = {A summary of team MIT{\textquoteright}s approach to the virtual robotics challenge},
	booktitle = {Robotics and Automation (ICRA), 2014 IEEE International Conference on},
	year = {2014},
	note = {[[{"fid":"3193356","view_mode":"default","type":"media","attributes":{"height":"360","width":"480","alt":"A Summary of Team MIT{\textquoteright}s Approach to the Virtual Robotics Challenge on YouTube","title":"A Summary of Team MIT{\textquoteright}s Approach to the Virtual Robotics Challenge","class":"media-element file-default"}}]]},
	pages = {2087{\textendash}2087},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {The paper describes the system developed by researchers from MIT for the Defense Advanced Research Projects Agency{\textquoteright}s (DARPA) Virtual Robotics Challenge (VRC), held in June 2013. The VRC was the first competition in the DARPA Robotics Challenge (DRC), a program that aims to {\textquoteleft}{\textquoteleft}develop ground robotic capabilities to execute complex tasks in dangerous, degraded, human-engineered environments{\textquoteright}{\textquoteright}. The VRC required teams to guide a model of Boston Dynamics{\textquoteright} humanoid robot, Atlas, through driving, walking, and manipulation tasks in simulation. Team MIT{\textquoteright}s user interface, the Viewer, provided the operator with a unified representation of all available information. A 3D rendering of the robot depicted its most recently estimated body state with respect to the surrounding environment, represented by point clouds and texture-mapped meshes as sensed by on-board LIDAR and fused over time.},
	isbn = {978-1-4799-3685-4},
	doi = {10.1109/ICRA.2014.6907140},
	author = {Russ Tedrake and Fallon, Maurice and Karumanchi, Sisir and Scott Kuindersma and Antone, Matthew and Schneider, Toby and Howard, Tom and Walter, M. and Dai, H. and Deits, R. and Fleder, M. and Fourie, D. and Hammoud, R. and Hemachandra, S. and Ilardi, P. and Perez-D{\textquoteright}Arpino, C. and Pillai, S and Valenzuela, A. and Cantu, C. and Dolan, C. and Evans, I. and Jorgensen, S. and Kristeller, J. and Shah, J.A. and Iagnemma, K. and Teller, S.}
}
@conference {465431,
	title = {Robot Learning: Some Recent Examples},
	booktitle = {Proceedings of the Sixteenth Yale Workshop on Adaptive and Learning Systems},
	year = {2013},
	pages = {71-76},
	abstract = {This paper provides a brief overview of three recent contributions to robot learning developed by researchers at the University of Massachusetts Amherst. The first is the use of policy search algorithms that exploit new techniques in nonparameteric heteroscedastic regression to directly model policy-dependent distribution of cost. Experiments demonstrate dynamic stabilization of a mobile manipulator through learning flexible, risk-sensitive policies in very few trials. The second contribution is a novel method for robot learning from unstructured demonstrations that permits intelligent sequencing of primitives to create novel, adaptive behavior. This is demonstrated on a furniture assembly task using the PR2 mobile manipulator. The third contribution is a robot system that autonomously acquires skills through interaction with its environment.\&nbsp;},
	author = {George Konidaris and Scott Kuindersma and Scott Niekum and Roderic Grupen and Andrew Barto}
}
@article {Kuindersma13,
	title = {Variable Risk Control via Stochastic Optimization},
	journal = {International Journal of Robotics Research},
	volume = {32},
	number = {7},
	year = {2013},
	note = {[[{"fid":3193396,"view_mode":"default","type":"media","attributes":{"height":"360","width":"480","alt":"uBot-5 Dynamic Lifting on YouTube","title":"uBot-5 Dynamic Lifting","class":"media-element file-default"}}]]
[[{"fid":3193261,"view_mode":"default","type":"media","attributes":{"height":"360","width":"480","alt":"uBot-5 risk-sensitive arm recovery on YouTube","title":"uBot-5 risk-sensitive arm recovery","class":"media-element file-default"}}]]},
	pages = {806{\textendash}825},
	abstract = {We present new global and local policy search algorithms suitable for problems with policy-dependent cost variance (or risk), a property present in many robot control tasks. These algorithms exploit new techniques in nonparameteric heteroscedastic regression to directly model the policy-dependent distribution of cost. For local search, the learned cost model can be used as a critic for performing risk-sensitive gradient descent. Alternatively, decision-theoretic criteria can be applied to globally select policies to balance exploration and exploitation in a principled way, or to perform greedy minimization with respect to various risk-sensitive criteria. This separation of learning and policy selection permits variable risk control, where risk sensitivity can be flexibly adjusted and appropriate policies can be selected at runtime without relearning. We describe experiments in dynamic stabilization and manipulation with a mobile manipulator that demonstrate learning of flexible, risk-sensitive policies in very few trials.},
	author = {Scott Kuindersma and Roderic Grupen and Andrew Barto}
}
@article {Konidaris12,
	title = {Robot learning from demonstration by constructing skill trees},
	journal = {The International Journal of Robotics Research},
	volume = {31},
	number = {3},
	year = {2012},
	note = {[[{"fid":3193286,"view_mode":"default","type":"media","attributes":{"height":"360","width":"480","alt":"Autonomous Robot Skill Acquisition - AAAI 2011 Best Student Video on YouTube","title":"Autonomous Robot Skill Acquisition - AAAI 2011 Best Student Video","class":"media-element file-default"}}]]},
	pages = {360{\textendash}375},
	abstract = {We describe CST, an online algorithm for constructing skill trees from demonstration trajectories. CST segments a demonstration trajectory into a chain of component skills, where each skill has a goal and is assigned a suitable abstraction from an abstraction library. These properties permit skills to be improved eciently using a policy learning algorithm. Chains from multiple demonstration trajectories are merged into a skill tree. We show that CST can be used to acquire skills from human demonstration in a dynamic continuous domain, and from both expert demonstration and learned control sequences on the uBot-5 mobile manipulator.},
	author = {George Konidaris and Scott Kuindersma and Roderic Grupen and Andrew Barto}
}
@conference {Kuindersma12b,
	title = {Variable Risk Dynamic Mobile Manipulation},
	booktitle = {RSS 2012 Workshop on Mobile Manipulation},
	year = {2012},
	note = {[[{"fid":"3193396","view_mode":"default","type":"media","attributes":{"height":"360","width":"480","alt":"uBot-5 Dynamic Lifting on YouTube","title":"uBot-5 Dynamic Lifting","class":"media-element file-default"}}]]},
	address = {Sydney, Australia},
	abstract = {The ability to operate effectively in a variety of contexts will be a critical attribute of deployed mobile manipulators. In general, a variety of properties, such as battery charge, workspace constraints, and the presence of dangerous obstacles, will determine the suitability of particular control policies. Some context changes will cause shifts in risk sensitivity, or tendency to seek or avoid policies with high performance variation. We describe a policy search algorithm designed to address the problem of variable risk control. We generalize the simple stochastic gradient descent update to the risk-sensitive case, and show that, under certain conditions, it leads to an unbiased estimate of the gradient of the risk-sensitive objective. We show that the local critic structure used in the update can be exploited to interweave offline and online search to select local greedy policies or quickly change risk sensitivity. We evaluate the algorithm in experiments with a dynamically stable mobile manipulator lifting a heavy liquid-filled bottle while balancing.},
	author = {Scott Kuindersma and Roderic Grupen and Andrew Barto}
}
@conference {Kuindersma12,
	title = {Variational Bayesian Optimization for Runtime Risk-Sensitive Control},
	booktitle = {Robotics: Science and Systems VIII (RSS)},
	year = {2012},
	note = {[[{"fid":"3193261","view_mode":"default","type":"media","attributes":{"height":"360","width":"480","alt":"uBot-5 risk-sensitive arm recovery on YouTube","title":"uBot-5 risk-sensitive arm recovery","class":"media-element file-default"}}]]},
	pages = {201{\textendash}206},
	address = {Sydney, Australia},
	abstract = {We present a new Bayesian policy search algorithm suitable for problems with policy-dependent cost variance, a property present in many robot control tasks. We extend recent work on variational heteroscedastic Gaussian processes to the optimization case to achieve efficient minimization of very noisy cost signals. In contrast to most policy search algorithms, our method explicitly models the cost variance in regions of low expected cost and permits runtime adjustment of risk sensitivity without relearning. Our experiments with artificial systems and a real mobile manipulator demonstrate that flexible risk-sensitive policies can be learned in very few trials.},
	author = {Scott Kuindersma and Roderic Grupen and Andrew Barto}
}
@conference {Konidaris11d,
	title = {Acquiring Transferrable Mobile Manipulation Skills},
	booktitle = {RSS 2011 Workshop on Mobile Manipulation: Learning to Manipulate},
	year = {2011},
	address = {Los Angeles, CA},
	abstract = {This abstract summarizes recent research on the autonomous acquisition of transferrable manipulation skills. We describe a robot system that learns to sequence a set of innate controllers to solve a task, and then extracts transferrable manipulation skills from the resulting solution. Using the extracted skills, the robot is able to significantly reduce the time required to discover the solution to a second task.},
	author = {Konidaris, George D and Kuindersma, Scott R and Grupen, Roderic A and Barto, Andrew G}
}
@conference {Konidaris11b,
	title = {Autonomous Skill Acquisition on a Mobile Manipulator},
	booktitle = {Proceedings of the Twenty-Fifth Conference on Artificial Intelligence (AAAI-11)},
	year = {2011},
	note = {AAAI 2011 Best Student Video
[[{"fid":"3193286","view_mode":"default","type":"media","attributes":{"height":"360","width":"480","alt":"Autonomous Robot Skill Acquisition - AAAI 2011 Best Student Video on YouTube","title":"Autonomous Robot Skill Acquisition - AAAI 2011 Best Student Video","class":"media-element file-default"}}]]},
	pages = {1468{\textendash}1473},
	address = {San Francisco, CA},
	abstract = {We describe a robot system that autonomously acquires skills through interaction with its environment. The robot learns to sequence the execution of a set of innate controllers to solve a task, extracts and retains components of that solution as portable skills, and then transfers those skills to reduce the time required to learn to solve a second task.},
	author = {George Konidaris and Scott Kuindersma and Roderic Grupen and Andrew Barto}
}
@conference {Konidaris11a,
	title = {CST: Constructing Skill Trees by Demonstration},
	booktitle = {Proceedings of the ICML Workshop on New Developments in Imitation Learning},
	year = {2011},
	address = {Bellevue, WA},
	abstract = {We describe recent work on CST, an online algorithm for constructing skill trees from demonstration trajectories. CST segments a demonstration trajectory into a chain of component skills, where each skill has a goal and is assigned a suitable abstraction from an abstraction library. These properties per- mit skills to be improved eciently using a policy learning algorithm. Chains from mul- tiple demonstration trajectories are merged into a skill tree. We describe applications of CST to acquiring skills from human demon- stration in a dynamic continuous domain and from both expert demonstration and learned control sequences on a mobile manipulator.},
	author = {Konidaris, George D and Kuindersma, Scott R and Grupen, Roderic A and Barto, Andrew G}
}
@conference {Kuindersma11,
	title = {Learning Dynamic Arm Motions for Postural Recovery},
	booktitle = {Proceedings of the 11th IEEE-RAS International Conference on Humanoid Robots},
	year = {2011},
	pages = {7{\textendash}12},
	address = {Bled, Slovenia},
	abstract = {The biomechanics community has recently made progress toward understanding the role of rapid arm movements in human stability recovery. However, comparatively little work has been done exploring this type of control in humanoid robots. We provide a summary of recent insights into the functional contributions of arm recovery motions in humans and experimentally demonstrate advantages of this behavior on a dynamically stable mobile manipulator. Using Bayesian optimization, the robot efficiently discovers policies that reduce total energy expenditure and recovery footprint, and increase ability to stabilize after large impacts.},
	author = {Scott Kuindersma and Roderic Grupen and Andrew Barto}
}
@conference {Kuindersma10a,
	title = {Control Model Learning for Whole-Body Mobile Manipulation},
	booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence},
	year = {2010},
	author = {Scott Kuindersma}
}
@conference {Konidaris10,
	title = {Constructing Skill Trees for Reinforcement Learning Agents from Demonstration Trajectories},
	booktitle = {Advances in Neural Information Processing Systems 23},
	year = {2010},
	pages = {1162{\textendash}1170},
	abstract = {We introduce CST, an algorithm for constructing skill trees from demonstration trajectories in continuous reinforcement learning domains. CST uses a changepoint detection method to segment each trajectory into a skill chain by detecting a change of appropriate abstraction, or that a segment is too complex to model as a single skill. The skill chains from each trajectory are then merged to form a skill tree. We demonstrate that CST constructs an appropriate skill tree that can be further refined through learning in a challenging continuous domain, and that it can be used to segment demonstration trajectories on a mobile manipulator into chains of skills where each skill is assigned an appropriate abstraction.},
	author = {George Konidaris and Scott Kuindersma and Andrew Barto and Roderic Grupen}
}
@conference {Kuindersma10,
	title = {Learning from a Single Demonstration: Motion Planning with Skill Segmentation},
	booktitle = {NIPS Workshop on Learning and Planning from Batch Time Series Data},
	year = {2010},
	note = {[[{"fid":"3193376","view_mode":"default","type":"media","attributes":{"height":"360","width":"480","alt":"Model-based learning from demonstration with the uBot-5 on YouTube","title":"Model-based learning from demonstration with the uBot-5","class":"media-element file-default"}}]]},
	address = {Vancouver, BC},
	abstract = {We propose an approach to control learning from demonstration that first segments demonstration trajectories to identify subgoals, then uses model-based con- trol methods to sequentially reach these subgoals to solve the overall task. Using this approach, we show that a mobile robot is able to solve a combined navigation and manipulation task robustly after observing only a single successful trajectory.},
	author = {Scott Kuindersma and George Konidaris and Roderic Grupen and Andrew Barto}
}
