Showing papers by "Jonas Buchli published in 2015"

Towards versatile legged robots through active impedance control

Abstract: Robots with legs and arms have the potential to support humans in dangerous, dull or dirty tasks. A major motivation behind research on such robots is their potential versatility. However, these robots come at a high price in mechanical and control complexity. Hence, until they can demonstrate a clear advantage over their simpler counterparts, robots with arms and legs will not fulfill their true potential. In this paper, we discuss the opportunities for versatile robots that arise by actively controlling the mechanical impedance of joints and particularly legs. In contrast to passive elements such as springs, active impedance is achieved by torque-controlled joints allowing real-time adjustment of stiffness and damping. Adjustable stiffness and damping in real-time is a fundamental building block towards versatility. Experiments with our 80 kg hydraulic quadruped robot HyQ demonstrate that active impedance alone i.e. no springs in the structure can successfully emulate passively compliant elements during highly dynamic locomotion tasks running, jumping and hopping; and that no springs are needed to protect the actuation system. Here we present results of a flying trot, also referred to as a running trot. To the best of the authors' knowledge this is the first time a flying trot has been successfully implemented on a robot without passive elements such as springs. A critical discussion on the pros and cons of active impedance concludes the paper. This article is an extension of our previous work presented at the International Symposium on Robotics Research ISRR 2013.

Model-Based Hydraulic Impedance Control for Dynamic Robots

Abstract: Increasingly, robots are designed to interact with the environment, including humans and tools. Legged robots, in particular, have to deal with environmental contacts every time they take a step. To handle these interactions properly, it is desirable to be able to set the robot's dynamic behavior, i.e., its impedance. In this contribution, we investigate the most relevant theoretical and practical aspects in impedance control using hydraulic actuators, ranging from the force dynamics analysis and model-based controller design to the overall stability and performance assessment. We present results with one leg of the quadruped robot HyQ and also highlight the influence of hardware parameters, such as valve bandwidth and inertia, in the impedance and force tracking. In addition, we demonstrate the capabilities of HyQ's actively compliant leg by experimentally comparing it with a passively compliant version of the same leg. With such a broad spectrum of analyses and discussions, this paper aims to serve as a practical and comprehensive guide for implementing high-performance impedance control on highly dynamic hydraulic robots.

Unified motion control for dynamic quadrotor maneuvers demonstrated on slung load and rotor failure tasks

Abstract: In recent years impressive results have been presented illustrating the potential of quadrotors to solve challenging tasks. Generally, the derivation of the controllers involve complex analytical manipulation of the dynamics and are very specific to the task at hand. In addition, most approaches construct a trajectory and then design a stabilizing controller in a separate step, whereas a fully optimal solution requires finding both simultaneously. In this paper, a generalized approach is presented using an iterative optimal control algorithm. A series of complex tasks are thus solved using the same algorithm without the need for manual manipulation of the system dynamics, heuristic simplifications, or manual trajectory generation. First, aggressive maneuvers are performed by requiring the quadrotor to pass with a slung load through a window not high enough for the load to pass while hanging straight down. Second, go-to-goal tasks with single and double rotor failure are demonstrated. The adaptability and applicability of this unified approach to such diverse tasks with a nonlinear, underactuated, constrained, and in the case of the slung load, hybrid quadrotor systems is thus shown.

Risk Sensitive, Nonlinear Optimal Control: Iterative Linear Exponential-Quadratic Optimal Control with Gaussian Noise

Abstract: In this contribution, we derive ILEG, an iterative algorithm to find risk sensitive solutions to nonlinear, stochastic optimal control problems. The algorithm is based on a linear quadratic approximation of an exponential risk sensitive nonlinear control problem. ILEG allows to find risk sensitive policies and thus generalizes previous algorithms to solve nonlinear optimal control based on iterative linear-quadratic methods. Depending on the setting of the parameter controlling the risk sensitivity, two different strategies on how to cope with the risk emerge. For positive-value parameters, the control policy uses high feedback gains whereas for negative-value parameters, it uses a robust feedforward control strategy (a robust plan) with low gains. These results are illustrated with a simple example. This note should be considered as a preliminary report.

Towards Industrial Robot Learning from Demonstration

Abstract: Learning from demonstration (LfD) provides an easy and intuitive way to program robot behaviours, potentially reducing development time and costs tremendously. This is especially appealing for manufacturers interested in using industrial manipulators for high-mix production, since this technique enables fast and flexible modifications to the robot behaviours and is thus suitable to teach the robot to perform a wide range of tasks regularly. We define a set of criteria to assess the applicability of state-of-the-art LfD frameworks in the industry. A three-stage LfD method is then proposed, which incorporates human-in-the-loop adaptation to iteratively correct a batch-learned policy to improve accuracy and precision. The system will then transit to open-loop execution of the task to enhance production speed, by removing the human teacher from the feedback loop. The proposed LfD framework addresses all criteria set in this work.

Dynamic Monitoring Reveals Motor Task Characteristics in Prehistoric Technical Gestures

Abstract: Reconstructing ancient technical gestures associated with simple tool actions is crucial for understanding the co-evolution of the human forelimb and its associated control-related cognitive functions on the one hand, and of the human technological arsenal on the other hand. Although the topic of gesture is an old one in Paleolithic archaeology and in anthropology in general, very few studies have taken advantage of the new technologies from the science of kinematics in order to improve replicative experimental protocols. Recent work in paleoanthropology has shown the potential of monitored replicative experiments to reconstruct tool-use-related motions through the study of fossil bones, but so far comparatively little has been done to examine the dynamics of the tool itself. In this paper, we demonstrate that we can statistically differentiate gestures used in a simple scraping task through dynamic monitoring. Dynamics combines kinematics (position, orientation, and speed) with contact mechanical parameters (force and torque). Taken together, these parameters are important because they play a role in the formation of a visible archaeological signature, use-wear. We present our new affordable, yet precise methodology for measuring the dynamics of a simple hide-scraping task, carried out using a pull-to (PT) and a push-away (PA) gesture. A strain gage force sensor combined with a visual tag tracking system records force, torque, as well as position and orientation of hafted flint stone tools. The set-up allows switching between two tool configurations, one with distal and the other one with perpendicular hafting of the scrapers, to allow for ethnographically plausible reconstructions. The data show statistically significant differences between the two gestures: scraping away from the body (PA) generates higher shearing forces, but requires greater hand torque. Moreover, most benchmarks associated with the PA gesture are more highly variable than in the PT gesture. These results demonstrate that different gestures used in ‘common’ prehistoric tasks can be distinguished quantitatively based on their dynamic parameters. Future research needs to assess our ability to reconstruct these parameters from observed use-wear patterns.

Event-based estimation and control for remote robot operation with reduced communication

Abstract: An event-based communication framework for remote operation of a robot via a bandwidth-limited network is proposed. The robot sends state and environment estimation data to the operator, and the operator transmits updated control commands or policies to the robot. Event-based communication protocols are designed to ensure that data is transmitted only when required: the robot sends new estimation data only if this yields a significant information gain at the operator, and the operator transmits an updated control policy only if this comes with a significant improvement in control performance. The developed framework is modular and can be used with any standard estimation and control algorithms. Simulation results of a robotic arm highlight its potential for an efficient use of limited communication resources, for example, in disaster-response scenarios such as the DARPA Robotics Challenge.

Evaluating direct transcription and nonlinear optimization methods for robot motion planning

Abstract: This paper studies existing direct transcription methods for trajectory optimization applied to robot motion planning. There are diverse alternatives for the implementation of direct transcription. In this study we analyze the effects of such alternatives when solving a robotics problem. Different parameters such as integration scheme, number of discretization nodes, initialization strategies and complexity of the problem are evaluated. We measure the performance of the methods in terms of computational time, accuracy and quality of the solution. Additionally, we compare two optimization methodologies frequently used to solve the transcribed problem, namely Sequential Quadratic Programming (SQP) and Interior Point Method (IPM). As a benchmark, we solve different motion tasks on an underactuated and non-minimal-phase ball-balancing robot with a 10 dimensional state space and 3 dimensional input space. Additionally, we validate the results on a simulated 3D quadrotor. Finally, as a verification of using direct transcription methods for trajectory optimization on real robots, we present hardware experiments on a motion task including path constraints and actuation limits.

An Open Source, Fiducial Based, Visual-Inertial State Estimation System.

Abstract: Many robotic tasks rely on the estimation of the location of moving bodies with respect to the robotic workspace. This information about the robots pose and velocities is usually either directly used for localization and control or utilized for verification. Often motion capture systems are used to obtain such a state estimation. However, these systems are very costly and limited in terms of workspace size and outdoor usage. Therefore, we propose a lightweight and easy to use, visual inertial Simultaneous Localization and Mapping approach that leverages paper printable artificial landmarks, so called fiducials. Results show that by fusing visual and inertial data, the system provides accurate estimates and is robust against fast motions. Continuous estimation of the fiducials within the workspace ensures accuracy and avoids additional calibration. By providing an open source implementation and various datasets including ground truth information, we enable other community members to run, test, modify and extend the system using datasets or their own robotic setups.

Projection based whole body motion planning for legged robots

Abstract: In this paper we present a new approach for dynamic motion planning for legged robots. We formulate a trajectory optimization problem based on a compact form of the robot dynamics. Such a form is obtained by projecting the rigid body dynamics onto the null space of the Constraint Jacobian. As consequence of the projection, contact forces are removed from the model but their effects are still taken into account. This approach permits to solve the optimal control problem of a floating base constrained multibody system while avoiding the use of an explicit contact model. We use direct transcription to numerically solve the optimization. As the contact forces are not part of the decision variables the size of the resultant discrete mathematical program is reduced and therefore solutions can be obtained in a tractable time. Using a predefined sequence of contact configurations (phases), our approach solves motions where contact switches occur. Transitions between phases are automatically resolved without using a model for switching dynamics. We present results on a hydraulic quadruped robot (HyQ), including single phase (standing, crouching) as well as multiple phase (rearing, diagonal leg balancing and stepping) dynamic motions.

Policy Learning with an Efficient Black-Box Optimization Algorithm

Abstract: Robotic learning on real hardware requires an efficient algorithm which minimizes the number of trials needed to learn an optimal policy. Prolonged use of hardware causes wear and tear on the system and demands more attention from an operator. To this end, we present a novel black-box optimization algorithm, Reward Optimization with Compact Kernels and fast natural gradient regression (ROCK⋆). Our algorithm immediately updates knowledge after a single trial and is able to extrapolate in a controlled manner. These features make fast and safe learning on real hardware possible. The performance of our method is evaluated with standard benchmark functions that are commonly used to test optimization algorithms. We also present three different robotic optimization examples using ROCK⋆. The first robotic example is on a simulated robot arm, the second is on a real articulated legged system, and the third is on a simulated quadruped robot with 12 actuated joints. ROCK⋆ outperforms the current state-of-the-art algorithms in all tasks sometimes even by an order of magnitude.

On the use of positive feedback for improved torque control

Abstract: This paper considers the torque control problem for robots with flexible joints driven by electrical actuators. It is shown that the achievable closed-loop tracking bandwidth using PI torque controllers may be limited due to transmission zeros introduced by the load dynamics. This limitation is overcome by using positive feedback from the load motion in unison with PI torque controllers. The positive feedback is given in terms of load velocity, acceleration and jerk. Stability conditions for designing decentralized PI torque controllers are derived in terms of Routh-Hurwitz criteria. Disturbance rejection properties of the closed system are characterized and an analysis is carried out investigating the use of approximate positive feedback by omitting acceleration and/or jerk signals. The results of this paper are illustrated for a two DoF (degrees of freedom) system. Experimental results for a one DoF system are also included.

Transparency control method for robotic devices and a control device therefor

Abstract: The transparency control method (100) for robotic devices where a master dynamics (91) controls a slave dynamics (81) to minimize interaction forces and/or lag, comprising feedback controllers (202, 203) and a feedforward controller (204) connected with the slave dynamics (81). The master (91) is configured to provide a master acceleration value (92) as input for the feedforward controller (204) and also to the feedback controller (203), and the slave (81) is configured to provide a slave acceleration value (82) as input for the feedback controller (203), wherein the two acceleration values are subtracted one from the other. The outputs of the slave dynamics (81) as well as of the master dynamics (91) are also connected with a state estimator module (201) providing an estimated interaction force value (51) as input for a force feedback controller (202).

An Open Source, Fiducial Based, Visual-Inertial Motion Capture System

Abstract: Many robotic tasks rely on the accurate localization of moving objects within a given workspace. This information about the objects' poses and velocities are used for control,motion planning, navigation, interaction with the environment or verification. Often motion capture systems are used to obtain such a state estimate. However, these systems are often costly, limited in workspace size and not suitable for outdoor usage. Therefore, we propose a lightweight and easy to use, visual-inertial Simultaneous Localization and Mapping approach that leverages cost-efficient, paper printable artificial landmarks, socalled fiducials. Results show that by fusing visual and inertial data, the system provides accurate estimates and is robust against fast motions and changing lighting conditions. Tight integration of the estimation of sensor and fiducial pose as well as extrinsics ensures accuracy, map consistency and avoids the requirement for precalibration. By providing an open source implementation and various datasets, partially with ground truth information, we enable community members to run, test, modify and extend the system either using these datasets or directly running the system on their own robotic setups.