Papers with video attachments

Director: A User Interface Designed for Robot Operation with Shared Autonomy
P. Marion, et al., “Director: A User Interface Designed for Robot Operation with Shared Autonomy,” Journal of Field Robotics, 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 interface
presents 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.


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Optimization and stabilization of trajectories for constrained dynamical systems
M. Posa, S. Kuindersma, and R. Tedrake, “Optimization and stabilization of trajectories for constrained dynamical systems,” in Proceedings of the International Conference on Robotics and Automation (ICRA), Stockholm, Sweden, 2016.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–-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.

Optimization-based locomotion planning, estimation, and control design for Atlas
S. Kuindersma, et al., “Optimization-based locomotion planning, estimation, and control design for Atlas,” Autonomous Robots, vol. 40, no. 3, pp. 429–455, 2016.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.

M. Fallon, et al., “An Architecture for Online Affordance-based Perception and Whole-body Planning,” Journal of Field Robotics, vol. 32, no. 2, pp. 229–254, 2015.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'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.

An Efficiently Solvable Quadratic Program for Stabilizing Dynamic Locomotion
S. Kuindersma, F. Permenter, and R. Tedrake, “An Efficiently Solvable Quadratic Program for Stabilizing Dynamic Locomotion,” in Proceedings of the International Conference on Robotics and Automation (ICRA), Hong Kong, China, 2014, pp. 2589–2594.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.

R. Tedrake, et al., “A summary of team MIT's approach to the virtual robotics challenge,” in Robotics and Automation (ICRA), 2014 IEEE International Conference on, 2014, pp. 2087–2087.Abstract

The paper describes the system developed by researchers from MIT for the Defense Advanced Research Projects Agency'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 ``develop ground robotic capabilities to execute complex tasks in dangerous, degraded, human-engineered environments''. The VRC required teams to guide a model of Boston Dynamics' humanoid robot, Atlas, through driving, walking, and manipulation tasks in simulation. Team MIT'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.

Variable Risk Control via Stochastic Optimization
S. Kuindersma, R. Grupen, and A. Barto, “Variable Risk Control via Stochastic Optimization,” International Journal of Robotics Research, vol. 32, no. 7, pp. 806–825, 2013.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.

G. Konidaris, S. Kuindersma, R. Grupen, and A. Barto, “Robot learning from demonstration by constructing skill trees,” The International Journal of Robotics Research, vol. 31, no. 3, pp. 360–375, 2012.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.

S. Kuindersma, R. Grupen, and A. Barto, “Variable Risk Dynamic Mobile Manipulation,” in RSS 2012 Workshop on Mobile Manipulation, Sydney, Australia, 2012.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.

Variational Bayesian Optimization for Runtime Risk-Sensitive Control
S. Kuindersma, R. Grupen, and A. Barto, “Variational Bayesian Optimization for Runtime Risk-Sensitive Control,” in Robotics: Science and Systems VIII (RSS), Sydney, Australia, 2012, pp. 201–206.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.

Autonomous Skill Acquisition on a Mobile Manipulator
G. Konidaris, S. Kuindersma, R. Grupen, and A. Barto, “Autonomous Skill Acquisition on a Mobile Manipulator,” in Proceedings of the Twenty-Fifth Conference on Artificial Intelligence (AAAI-11), San Francisco, CA, 2011, pp. 1468–1473.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.

S. Kuindersma, R. Grupen, and A. Barto, “Learning Dynamic Arm Motions for Postural Recovery,” in Proceedings of the 11th IEEE-RAS International Conference on Humanoid Robots, Bled, Slovenia, 2011, pp. 7–12.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.

S. Kuindersma, G. Konidaris, R. Grupen, and A. Barto, “Learning from a Single Demonstration: Motion Planning with Skill Segmentation,” in NIPS Workshop on Learning and Planning from Batch Time Series Data, Vancouver, BC, 2010.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.

S. R. Kuindersma, E. Hannigan, D. Ruiken, and R. A. Grupen, “Dexterous mobility with the uBot-5 mobile manipulator,” in Proceedings of the 14th International Conference on Advanced Robotics, Munich, Germany, 2009.Abstract

We present an initial demonstration of dexterous mobility using the uBot-5, a dynamically balancing mobile manipulator. Dexterous mobility refers generally to a level of bodily resourcefulness that permits the autonomous reassignment of effectors for the purpose of maintaining mobility in a variety of situations. We begin by describing a set of postural stability controllers in terms of a small number of simple control objectives. We then show how the resulting postures support dexterous mobility by enabling a new ldquoknuckle walkingrdquo mobility mode. In a preliminary experiment, we develop this mobility mode by formulating a practical reinforcement learning problem that allows the robot to learn an efficient gait on-line in a single trial.