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A Convex Model of Humanoid Momentum Dynamics for Multi-Contact Motion Generation

Ponton, B., Herzog, A., Schaal, S., Righetti, L.

Proceedings of the 2016 IEEE-RAS International Conference on Humanoid Robots, 2016 (conference)

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arxiv video [BibTex]

arxiv video [BibTex]


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Stepping stabilization using a combination of DCM tracking and step adjustment

Khadiv, M., Kleff, S., Herzog, A., Moosavian, S., Schaal, S., Righetti, L.

4th RSI International Conference on Robotics and Mechatronics, 2016 (conference)

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arxiv [BibTex]

arxiv [BibTex]


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Structured contact force optimization for kino-dynamic motion generation

Herzog, A., Schaal, S., Righetti, L.

In International Conference on Intelligent Robots and Systems (IROS) 2016, pages: 2703-2710, IEEE/RSJ International Conference on Intelligent Robots and Systems, 2016 (inproceedings)

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video pdf link (url) [BibTex]

video pdf link (url) [BibTex]


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Step Timing Adjustment: A Step Toward Generating Robust Gaits

Khadiv, M., Herzog, A., Moosavian, S., Righetti, L.

Proceedings of the 2016 IEEE-RAS International Conference on Humanoid Robots, 2016 (conference)

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arxiv video [BibTex]

arxiv video [BibTex]


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Balancing and Walking Using Full Dynamics LQR Control with Contact Constraints

Mason, S., rotella, N., Schaal, S., Righetti, L.

Proceedings of the 2016 IEEE-RAS International Conference on Humanoid Robots, 2016 (conference)

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[BibTex]

[BibTex]

2015


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Trajectory generation for multi-contact momentum-control

Herzog, A., Rotella, N., Schaal, S., Righetti, L.

In IEEE-RAS International Conference on Humanoid Robots (Humanoids), November 2015 (inproceedings)

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link (url) Project Page [BibTex]

2015


link (url) Project Page [BibTex]


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Kinematic and gait similarities between crawling human infants and other quadruped mammals

Righetti, L., Nylen, A., Rosander, K., Ijspeert, A.

Frontiers in Neurology, 6(17), Febuary 2015 (article)

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[BibTex]

[BibTex]


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From Humans to Robots and Back: Role of Arm Movement in Medio-lateral Balance Control

Huber, M. E., Chiovetto, E., Righetti, L., Schaal, S., Giese, M., Sternad, D.

In Proceedings of Dynamic Walking, 2015 (inproceedings)

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[BibTex]

[BibTex]


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Full Dynamics LQR Control for Bipedal Walking

Mason, S., Schaal, S., Righetti, L.

Proceedings of Dynamic Walking, 2015 (conference)

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[BibTex]

[BibTex]


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Humanoid Momentum Estimation Using Sensed Contact Wrenches

Rotella, N., Herzog, A., Schaal, S., Righetti, L.

Proceedings of the 2015 IEEE-RAS International Conference on Humanoid Robots, 2015 (conference)

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Project Page [BibTex]

Project Page [BibTex]


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Wrong and Useful: Metrics to Assess Simple Walking Models

Rebula, J., Righetti, L., Schaal, S.

In Proceedings of Dynamic Walking, 2015 (inproceedings)

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[BibTex]

[BibTex]

2014


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Dual Execution of Optimized Contact Interaction Trajectories

Toussaint, M., Ratliff, N., Bohg, J., Righetti, L., Englert, P., Schaal, S.

In Proceedings of the International Conference on Intelligent Robots and Systems, Chicago, IL, October 2014 (inproceedings)

Abstract
Efficient manipulation requires contact to reduce uncertainty. The manipulation literature refers to this as funneling: a methodology for increasing reliability and robustness by leveraging haptic feedback and control of environmental interaction. However, there is a fundamental gap between traditional approaches to trajectory optimization and this concept of robustness by funneling: traditional trajectory optimizers do not discover force feedback strategies. From a POMDP perspective, these behaviors could be regarded as explicit obser- vation actions planned to sufficiently reduce uncertainty thereby enabling a task. While we are sympathetic to the full POMDP view, solving full continuous-space POMDPs in high-dimensions is hard. In this paper, we propose an alternative approach in which trajectory optimization objectives are augmented with new terms that reward uncertainty reduction through contacts, explicitly promoting funneling. This augmentation shifts the responsibility of robustness toward the actual execution of the optimized trajectories. Directly tracing trajectories through configuration space would lose all robustnessâ??dual execution achieves robustness by devising force controllers to reproduce the temporal interaction profile encoded in the dual solution of the optimization problem. This work introduces dual execution in depth and analyze its performance through robustness experiments in both simulation and on a real-world robotic platform.

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PDF Video DOI Project Page [BibTex]

2014


PDF Video DOI Project Page [BibTex]


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Balancing experiments on a torque-controlled humanoid with hierarchical inverse dynamics

Herzog, A., Righetti, L., Grimminger, F., Pastor, P., Schaal, S.

Proceedings of the IEEE International Conference on Intelligent Robotics Systems, Chicago, IL, September 2014 (conference)

Abstract
Recently several hierarchical inverse dynamicscontrollers based on cascades of quadratic programs havebeen proposed for application on torque controlled robots.They have important theoretical benefits but have never beenimplemented on a torque controlled robot where model inaccuraciesand real-time computation requirements can beproblematic. In this contribution we present an experimentalevaluation of these algorithms in the context of balance controlfor a humanoid robot. The presented experiments demonstratethe applicability of the approach under real robot conditions(i.e. model uncertainty, estimation errors, etc). We propose asimplification of the optimization problem that allows us todecrease computation time enough to implement it in a fasttorque control loop. We implement a momentum-based balancecontroller which shows robust performance in face of unknowndisturbances, even when the robot is standing on only onefoot. In a second experiment, a tracking task is evaluatedto demonstrate the performance of the controller with morecomplicated hierarchies. Our results show that hierarchicalinverse dynamics controllers can be used for feedback controlof humanoid robots and that momentum-based balance controlcan be efficiently implemented on a real robot.

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Video pdf DOI Project Page [BibTex]

Video pdf DOI Project Page [BibTex]


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An autonomous manipulation system based on force control and optimization

Righetti, L., Kalakrishnan, M., Pastor, P., Binney, J., Kelly, J., Voorhies, R. C., Sukhatme, G. S., Schaal, S.

Autonomous Robots, 36(1-2):11-30, Springer US, Febuary 2014, clmc (article)

Abstract
In this paper we present an architecture for autonomous manipulation. Our approach is based on the belief that contact interactions during manipulation should be exploited to improve dexterity and that optimizing motion plans is useful to create more robust and repeatable manipu- lation behaviors. We therefore propose an architecture where state of the art force/torque control and optimization-based motion planning are the core components of the system. We give a detailed description of the modules that constitute the complete system and discuss the challenges inherent to creat- ing such a system. We present experimental results for several grasping and manipulation tasks to demonstrate the perfor- mance and robustness of our approach.

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Web Video link (url) DOI Project Page [BibTex]

Web Video link (url) DOI Project Page [BibTex]


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Learning of Grasp Selection based on Shape-Templates

Herzog, A., Pastor, P., Kalakrishnan, M., Righetti, L., Bohg, J., Asfour, T., Schaal, S.

Autonomous Robots, 36(1-2):51-65, Springer US, January 2014 (article)

Abstract
The ability to grasp unknown objects still remains an unsolved problem in the robotics community. One of the challenges is to choose an appropriate grasp configu- ration, i.e., the 6D pose of the hand relative to the object and its finger configuration. In this paper, we introduce an algo- rithm that is based on the assumption that similarly shaped objects can be grasped in a similar way. It is able to synthe- size good grasp poses for unknown objects by finding the best matching object shape templates associated with previously demonstrated grasps. The grasp selection algorithm is able to improve over time by using the information of previous grasp attempts to adapt the ranking of the templates to new situa- tions. We tested our approach on two different platforms, the Willow Garage PR2 and the Barrett WAM robot, which have very different hand kinematics. Furthermore, we compared our algorithm with other grasp planners and demonstrated its superior performance. The results presented in this paper show that the algorithm is able to find good grasp configura- tions for a large set of unknown objects from a relatively small set of demonstrations, and does improve its performance over time.

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video pdf DOI Project Page Project Page [BibTex]


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Experiments with Hierarchical Inverse Dynamics Controllers on a Torque Controlled Humanoid

Herzog, A., Righetti, L., Grimminger, F., Schaal, S.

In Proceedings of Dynamic Walking, Zürich, Switzerland, 2014, clmc (inproceedings)

Abstract
We expect autonomous legged robots to perform complex tasks in persistent interaction with an uncertain and changing environment (e.g. in a disaster relief scenario). Therefore, we need to design algorithms that can generate precise but compliant motions while optimizing the interactions with the environment. In this context, torque control algorithms often offer high performance for motion control while guaranteeing a certain level of compliance. In addition they allow for direct control of interaction forces with the environment. Recent contributions have demonstrated the relevance of torque con- trol approaches for humanoid robots, for example for balanc- ing capabilities [5, 6]. Among those we find passivity-based approaches [5] that regulate the position of the Center of Mass (CoM) by applying admissible contact forces under the quasi- static assumption. On the one hand, these approaches do not rely on a precise dynamic model of the robot while natu- rally guaranteeing robustness due to the passivity property of the controller. On the other hand the quasi-static assumption might be limiting for dynamic motions. A promising way of leveraging this limitation are control algorithms that take the full dynamic model into account [6]. However, the need for a precise dynamic model, sensor noise (particularly in the ve- locities) and limited torque bandwidth makes them more chal- lenging to implement. Moreover, it is generally required to simplify the optimization process to meet time requirements of fast control loops (typically 1 kHz on modern torque con- trolled robots). Practical evaluations of both approaches are still rare due to the lack of torque controlled humanoid plat- forms and the complexity in conducting such robot experiments.

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PDF link (url) [BibTex]


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Full Dynamics LQR Control of a Humanoid Robot: An Experimental Study on Balancing and Squatting

Mason, S., Righetti, L., Schaal, S.

In 14th IEEE-RAS International Conference on Humanoid Robots (Humanoids), 2014 (inproceedings)

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Project Page [BibTex]

Project Page [BibTex]


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Towards Full System Linear Quadratic Regulators for Humanoid Control

Mason, S., Righetti, L., Schaal, S.

In Proceedings of Dynamic Walking, Zurich, Switzerland, 2014, clmc (inproceedings)

Abstract
Robots that are to locomote in a human like fashion requirecontrol of high degree of freedom (DOF) motions potentiallycoupled in a complex way. It remains challenging to expressthe task objective in an intuitive way and simultaneously generatefeedback gains guaranteeing some level of optimality.In response to this, a number of different simplified modelshave been developed to highlight different aspects of the humanoidâ??sdynamics that are important for specific tasks. Ashort list of some of the models used to represent a humanoidinclude the cart-table, double inverted pendulum, reactionmass pendulum, and automatically generated task specific reducedmodels [4]. These simplified models make planningeasier but come at the cost of modelling error and limitationson motion. In addition, one is tasked with finding mappingsbetween the full system to the reduced system. These mappingscan potentially destroy the intuition surrounding the useof the simplified model as they may not always behave as expected.By working with the full dynamics, one obtains anincrease in generality, accuracy, and eliminates the need formappings.

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PDF link (url) [BibTex]

PDF link (url) [BibTex]


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State Estimation for a Humanoid Robot

Rotella, N., Bloesch, M., Righetti, L., Schaal, S.

In Proceedings of the 2014 IEEE/RSJ Conference on Intelligent Robots and Systems, pages: 952-958, Chicago, IL, 2014 (inproceedings)

Abstract
This paper introduces a framework for state estimation on a humanoid robot platform using only common proprioceptive sensors and knowledge of leg kinematics. The presented approach extends that detailed in prior work on a point-foot quadruped platform by adding the rotational constraints imposed by the humanoidâ??s flat feet. As in previous work, the proposed Extended Kalman Filter accommodates contact switching and makes no assumptions about gait or terrain, making it applicable on any humanoid platform for use in any task. A nonlinear observability analysis is performed on both the point-foot and flat-foot filters and it is concluded that the addition of rotational constraints significantly simplifies singular cases and improves the observability characteristics of the system. Results on a simulated walking dataset demonstrate the performance gain of the flat-foot filter as well as confirm the results of the presented observability analysis.

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PDF link (url) Project Page [BibTex]

PDF link (url) Project Page [BibTex]


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State Estimation for Walking Humanoids on Unknown Terrain

Rotella, N., Bloesch, M., Righetti, L., Schaal, S.

In Proceedings of Dynamic Walking, Zürich, Switzerland, 2014, clmc (inproceedings)

Abstract
State estimation plays a crucial role in humanoid locomotion;accurate estimates of the pose and velocity of the robotâ??s baseare necessary for walking tasks. Estimation in robotics haslong been focused on mobile robot localization, where wheelodometry and exteroceptive sensor data are fused to provideestimates of absolute position and yaw. While wheeled robotsare assumed to remain stable and in contact at all times,legged locomotion inherently involves intermittent contacts.This makes stability a concern and complicates odometrybasedapproaches, distinguishing estimation for legged systemsfrom that for wheeled robots. More recent approacheson quadruped and hexapod platforms make unreasonable assumptionsabout walking gaits, assume knowledge of the terrainand use exteroceptive sensor data for corrections. However,the utility of such platforms is their potential for operationin unstructured environments in which gaits are reactive,the terrain is unknown and such sensors are unfit for use. Motivatedby the task of providing robust and generic state estimationfor humanoid robots walking on unknown terrain, weintroduce an estimation framework [1] which employs onlyproprioceptive sensors and knowledge of leg kinematics.

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PDF link (url) [BibTex]

PDF link (url) [BibTex]

2013


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Controlled Reduction with Unactuated Cyclic Variables: Application to 3D Bipedal Walking with Passive Yaw Rotation

Gregg, R., Righetti, L.

IEEE Transactions on Automatic Control, 58(10):2679-2685, October 2013 (article)

am mg

[BibTex]

2013


[BibTex]


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AGILITY – Dynamic Full Body Locomotion and Manipulation with Autonomous Legged Robots

Hutter, M., Bloesch, M., Buchli, J., Semini, C., Bazeille, S., Righetti, L., Bohg, J.

In IEEE International Symposium on Safety, Security, and Rescue Robotics, pages: 1-4, Linköping, 2013 (inproceedings)

am mg

[BibTex]

[BibTex]


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Markov Random Fields for Stochastic Trajectory Optimization and Learning with Constraints

Kalakrishnan, M., Herzog, A., Righetti, L., Schaal, S.

In Workshop: Hierarchical and Structured Learning for Robotics, RSS 2013, pages: 1-1, Berlin, 2013 (inproceedings)

am mg

[BibTex]

[BibTex]


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Optimal distribution of contact forces with inverse dynamics control

Righetti, L., Buchli, J., Mistry, M., Kalakrishnan, M., Schaal, S.

The International Journal of Robotics Research, 32(3):280-298, 2013, clmc (article)

Abstract
The development of legged robots for complex environments requires controllers that guarantee both high tracking performance and compliance with the environment. More specifically the control of the contact interaction with the environment is of crucial importance to ensure stable, robust and safe motions. In this contribution we develop an inverse-dynamics controller for floating-base robots under contact constraints that can minimize any combination of linear and quadratic costs in the contact constraints and the commands. Our main result is the exact analytical derivation of the controller. Such a result is particularly relevant for legged robots as it allows us to use torque redundancy to directly optimize contact interactions. For example, given a desired locomotion behavior, we can guarantee the minimization of contact forces to reduce slipping on difficult terrains while ensuring high tracking performance of the desired motion. The main advantages of the controller are its simplicity, computational efficiency and robustness to model inaccuracies. We present detailed experimental results on simulated humanoid and quadruped robots as well as a real quadruped robot. The experiments demonstrate that the controller can greatly improve the robustness of locomotion of the robots.

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PDF Project Page Project Page [BibTex]

PDF Project Page Project Page [BibTex]


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Learning Task Error Models for Manipulation

Pastor, P., Kalakrishnan, M., Binney, J., Kelly, J., Righetti, L., Sukhatme, G., Schaal, S.

In IEEE International Conference on Robotics and Automation, 2013 (inproceedings)

Abstract
Precise kinematic forward models are important for robots to successfully perform dexterous grasping and manipula- tion tasks, especially when visual servoing is rendered infeasible due to occlusions. A lot of research has been conducted to estimate geometric and non-geometric parameters of kinematic chains to minimize reconstruction errors. However, kinematic chains can include non-linearities, e.g. due to cable stretch and motor-side encoders, that result in significantly different errors for different parts of the state space. Previous work either does not consider such non-linearities or proposes to estimate non-geometric parameters of carefully engineered models that are robot specific. We propose a data-driven approach that learns task error models that account for such unmodeled non-linearities. We argue that in the context of grasping and manipulation, it is sufficient to achieve high accuracy in the task relevant state space. We identify this relevant state space using previously executed joint configurations and learn error corrections for those. Therefore, our system is developed to generate subsequent executions that are similar to previous ones. The experiments show that our method successfully captures the non-linearities in the head kinematic chain (due to a counter- balancing spring) and the arm kinematic chains (due to cable stretch) of the considered experimental platform, see Fig. 1. The feasibility of the presented error learning approach has also been evaluated in independent DARPA ARM-S testing contributing to successfully complete 67 out of 72 grasping and manipulation tasks.

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PDF video Project Page [BibTex]

PDF video Project Page [BibTex]


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Using Torque Redundancy to Optimize Contact Forces in Legged Robots

Righetti, L., Buchli, J., Mistry, M., Kalakrishnan, M., Schaal, S.

In Redundancy in Robot Manipulators and Multi-Robot Systems, pages: 35-51, (Editors: Dejan Milutinović and Jacob Rosen), Springer-Verlag Berlin Heidelberg, 2013, clmc (inbook)

Abstract
The development of legged robots for complex environments requires controllers that guarantee both high tracking performance and compliance with the environment. More specifically the control of contact interaction with the environment is of crucial importance to ensure stable, robust and safe motions. In the following, we present an inverse dynamics controller that exploits torque redundancy to directly and explicitly minimize any combination of linear and quadratic costs in the contact constraints and in the commands. Such a result is particularly relevant for legged robots as it allows to use torque redundancy to directly optimize contact interactions. For example, given a desired locomotion behavior, it can guarantee the minimization of contact forces to reduce slipping on difficult terrains while ensuring high tracking performance of the desired motion. The proposed controller is very simple and computationally efficient, and most importantly it can greatly improve the performance of legged locomotion on difficult terrains as can be seen in the experimental results.

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PDF [BibTex]

PDF [BibTex]


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Learning Objective Functions for Manipulation

Kalakrishnan, M., Pastor, P., Righetti, L., Schaal, S.

In IEEE International Conference on Robotics and Automation, 2013, clmc (inproceedings)

Abstract
We present an approach to learning objective func- tions for robotic manipulation based on inverse reinforcement learning. Our path integral inverse reinforcement learning al- gorithm can deal with high-dimensional continuous state-action spaces, and only requires local optimality of demonstrated trajectories. We use L1 regularization in order to achieve feature selection, and propose an efficient algorithm to minimize the re- sulting convex objective function. We demonstrate our approach by applying it to two core problems in robotic manipulation. First, we learn a cost function for redundancy resolution in inverse kinematics. Second, we use our method to learn a cost function over trajectories, which is then used in optimization- based motion planning for grasping and manipulation tasks. Experimental results show that our method outperforms previous algorithms in high-dimensional settings.

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PDF Project Page Project Page [BibTex]

PDF Project Page Project Page [BibTex]

2012


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Template-based learning of grasp selection

Herzog, A., Pastor, P., Kalakrishnan, M., Righetti, L., Asfour, T., Schaal, S.

In IEEE International Conference on Robotics and Automation (ICRA), pages: 2379-2384, May 2012 (inproceedings)

Abstract
The ability to grasp unknown objects is an important skill for personal robots, which has been addressed by many present and past research projects, but still remains an open problem. A crucial aspect of grasping is choosing an appropriate grasp configuration, i.e. the 6d pose of the hand relative to the object and its finger configuration. Finding feasible grasp configurations for novel objects, however, is challenging because of the huge variety in shape and size of these objects. Moreover, possible configurations also depend on the specific kinematics of the robotic arm and hand in use. In this paper, we introduce a new grasp selection algorithm able to find object grasp poses based on previously demonstrated grasps. Assuming that objects with similar shapes can be grasped in a similar way, we associate to each demonstrated grasp a grasp template. The template is a local shape descriptor for a possible grasp pose and is constructed using 3d information from depth sensors. For each new object to grasp, the algorithm then finds the best grasp candidate in the library of templates. The grasp selection is also able to improve over time using the information of previous grasp attempts to adapt the ranking of the templates. We tested the algorithm on two different platforms, the Willow Garage PR2 and the Barrett WAM arm which have very different hands. Our results show that the algorithm is able to find good grasp configurations for a large set of objects from a relatively small set of demonstrations, and does indeed improve its performance over time. View full abstract

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video pdf DOI Project Page [BibTex]

2012


video pdf DOI Project Page [BibTex]


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Learning Force Control Policies for Compliant Robotic Manipulation

Kalakrishnan, M., Righetti, L., Pastor, P., Schaal, S.

In International Conference on Machine Learning (ICML), 2012, clmc (inproceedings)

Abstract
In this abstract, we present an approach to learning manipulation tasks on compliant robots through re- inforcement learning. We demonstrate our approach on two different manipulation tasks: opening a door with a lever door handle, and picking up a pen off the table (Fig. 1). We show that our approach can learn the force control policies required to achieve both tasks successfully. The contributions of this work are two-fold: (1) we demonstrate that learning force con- trol policies enables compliant execution of manipu- lation tasks with increased robustness as opposed to stiff position control, and (2) we introduce a policy parameterization that uses finely discretized trajectories coupled with a cost function that ensures smoothness during exploration and learning.

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link (url) [BibTex]

link (url) [BibTex]


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Towards Associative Skill Memories

Pastor, P., Kalakrishnan, M., Righetti, L., Schaal, S.

In IEEE-RAS International Conference on Humanoid Robots, 2012, clmc (inproceedings)

Abstract
Movement primitives as basis of movement planning and control have become a popular topic in recent years. The key idea of movement primitives is that a rather small set of stereotypical movements should suffice to create a large set of complex manipulation skills. An interesting side effect of stereotypical movement is that it also creates stereotypical sensory events, e.g., in terms of kinesthetic variables, haptic variables, or, if processed appropriately, visual variables. Thus, a movement primitive executed towards a particular object in the environment will associate a large number of sensory variables that are typical for this manipulation skill. These association can be used to increase robustness towards perturbations, and they also allow failure detection and switching towards other behaviors. We call such movement primitives augmented with sensory associations {em Associative Skill Memories} (ASM). This paper addresses how ASMs can be acquired by imitation learning and how they can create robust manipulation skill by determining subsequent ASMs extit{online} to achieve a particular manipulation goal. Evaluation for grasping and manipulation with a Barrett WAM/Hand illustrate our approach.

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PDF Project Page [BibTex]

PDF Project Page [BibTex]


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Probabilistic depth image registration incorporating nonvisual information

Wüthrich, M., Pastor, P., Righetti, L., Billard, A., Schaal, S.

In IEEE International Conference on Robotics and Automation (ICRA), pages: 3637-3644, 2012, clmc (inproceedings)

Abstract
In this paper, we derive a probabilistic registration algorithm for object modeling and tracking. In many robotics applications, such as manipulation tasks, nonvisual information about the movement of the object is available, which we will combine with the visual information. Furthermore we do not only consider observations of the object, but we also take space into account which has been observed to not be part of the object. Furthermore we are computing a posterior distribution over the relative alignment and not a point estimate as typically done in for example Iterative Closest Point (ICP). To our knowledge no existing algorithm meets these three conditions and we thus derive a novel registration algorithm in a Bayesian framework. Experimental results suggest that the proposed methods perform favorably in comparison to PCL [1] implementations of feature mapping and ICP, especially if nonvisual information is available. View full abstract

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Web DOI [BibTex]

Web DOI [BibTex]


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Quadratic programming for inverse dynamics with optimal distribution of contact forces

Righetti, L., Schaal, S.

In 2012 IEEE-RAS International Conference on Humanoid Robots, pages: 538-543, Osaka, 2012, clmc (inproceedings)

Abstract
In this contribution we propose an inverse dynamics controller for a humanoid robot that exploits torque redundancy to minimize any combination of linear and quadratic costs in the contact forces and the commands. In addition the controller satisfies linear equality and inequality constraints in the contact forces and the commands such as torque limits, unilateral contacts or friction cones limits. The originality of our approach resides in the formulation of the problem as a quadratic program where we only need to solve for the control commands and where the contact forces are optimized implicitly. Furthermore, we do not need a structured representation of the dynamics of the robot (i.e. an explicit computation of the inertia matrix). It is in contrast with existing methods based on quadratic programs. The controller is then robust to uncertainty in the estimation of the dynamics model and the optimization is fast enough to be implemented in high bandwidth torque control loops that are increasingly available on humanoid platforms. We demonstrate properties of our controller with simulations of a human size humanoid robot.

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PDF [BibTex]

PDF [BibTex]


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Encoding of Periodic and their Transient Motions by a Single Dynamic Movement Primitive

Ernesti, J., Righetti, L., Do, M., Asfour, T., Schaal, S.

In 2012 IEEE-RAS International Conference on Humanoid Robots, pages: 57-64, Osaka, 2012, clmc (inproceedings)

Abstract
Present formulations of periodic dynamic move- ment primitives (DMPs) do not encode the transient behavior required to start the rhythmic motion, although these transient movements are an important part of the rhythmic movements (i.e. when walking, there is always a first step that is very different from the subsequent ones). An ad-hoc procedure is then necessary to get the robot into the periodic motion. In this contribution we present a novel representation for rhythmic Dynamic Movement Primitives (DMPs) that encodes both the rhythmic motion and its transient behaviors. As with previously proposed DMPs, we use a dynamical system approach where an asymptotically stable limit cycle represents the periodic pattern. Transients are then represented as trajectories converg- ing towards the limit cycle, different trajectories representing varying transients from different initial conditions. Our ap- proach thus constitutes a generalization of previously proposed rhythmic DMPs. Experiments conducted on the humanoid robot ARMAR-III demonstrate the applicability of the approach for movement generation.

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PDF Project Page [BibTex]

PDF Project Page [BibTex]

2011


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Learning force control policies for compliant manipulation

Kalakrishnan, M., Righetti, L., Pastor, P., Schaal, S.

In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011), Sept. 25-30, San Francisco, CA, 2011, clmc (inproceedings)

Abstract
Developing robots capable of fine manipulation skills is of major importance in order to build truly assistive robots. These robots need to be compliant in their actuation and control in order to operate safely in human environments. Manip-ulation tasks imply complex contact interactions with the external world, and in-volve reasoning about the forces and torques to be applied. Planning under con-tact conditions is usually impractical due to computational complexity, and a lack of precise dynamics models of the environment. We present an approach to acquiring manipulation skills on compliant robots through reinforcement learn-ing. The initial position control policy for manipulation is initialized through kinesthetic demonstration. We augment this policy with a force/torque profile to be controlled in combination with the position trajectories. We use the Policy Improvement with Path Integrals (PI2) algorithm to learn these force/torque pro-files by optimizing a cost function that measures task success. We demonstrate our approach on the Barrett WAM robot arm equipped with a 6-DOF force/torque sensor on two different manipulation tasks: opening a door with a lever door handle, and picking up a pen off the table. We show that the learnt force control policies allow successful, robust execution of the tasks.

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link (url) Project Page [BibTex]

2011


link (url) Project Page [BibTex]


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Online movement adaptation based on previous sensor experiences

Pastor, P., Righetti, L., Kalakrishnan, M., Schaal, S.

In IEEE International Conference on Intelligent Robots and Systems (IROS), pages: 365-371, 2011 (inproceedings)

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Project Page Project Page Project Page [BibTex]

Project Page Project Page Project Page [BibTex]


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Toward simple control for complex, autonomous robotic applications: combining discrete and rhythmic motor primitives

Degallier, S., Righetti, L., Gay, S., Ijspeert, A.

Autonomous Robots, 31(2-3):155-181, 2011 (article)

am mg

[BibTex]

[BibTex]


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Inverse dynamics control of floating-base robots with external contraints: an unified view

Righetti, L., Mistry, M., Buchli, J., Schaal, S.

In IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China, May 9-13, 2011, clmc (inproceedings)

Abstract
Inverse dynamics controllers and operational space controllers have proved to be very efficient for compliant control of fully actuated robots such as fixed base manipulators. However legged robots such as humanoids are inherently different as they are underactuated and subject to switching external contact constraints. Recently several methods have been proposed to create inverse dynamics controllers and operational space controllers for these robots. In an attempt to compare these different approaches, we develop a general framework for inverse dynamics control and show that these methods lead to very similar controllers. We are then able to greatly simplify recent whole-body controllers based on operational space approaches using kinematic projections, bringing them closer to efficient practical implementations. We also generalize these controllers such that they can be optimal under an arbitrary quadratic cost in the commands.

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link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Operational Space Control of Constrained and Underactuated Systems

Mistry, M., Righetti, L.

In Proceedings of Robotics: Science and Systems, Los Angeles, CA, USA, 2011 (inproceedings)

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Project Page Project Page [BibTex]

Project Page Project Page [BibTex]


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Control of legged robots with optimal distribution of contact forces

Righetti, L., Buchli, J., Mistry, M., Schaal, S.

In 2011 11th IEEE-RAS International Conference on Humanoid Robots, pages: 318 - 324, 2011, clmc (inproceedings)

Abstract
The development of agile and safe humanoid robots require controllers that guarantee both high tracking performance and compliance with the environment. More specifically, the control of contact interaction is of crucial importance for robots that will actively interact with their environment. Model-based controllers such as inverse dynamics or operational space control are very appealing as they offer both high tracking performance and compliance. However, while widely used for fully actuated systems such as manipulators, they are not yet standard controllers for legged robots such as humanoids. Indeed such robots are fundamentally different from manipulators as they are underactuated due to their floating-base and subject to switching contact constraints. In this paper we present an inverse dynamics controller for legged robots that use torque redundancy to create an optimal distribution of contact constraints. The resulting controller is able to minimize, given a desired motion, any quadratic cost of the contact constraints at each instant of time. In particular we show how this can be used to minimize tangential forces during locomotion, therefore significantly improving the locomotion of legged robots on difficult terrains. In addition to the theoretical result, we present simulations of a humanoid and a quadruped robot, as well as experiments on a real quadruped robot that demonstrate the advantages of the controller.

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PDF [BibTex]

PDF [BibTex]


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Learning motion primitive goals for robust manipulation

Stulp, F., Theodorou, E., Kalakrishnan, M., Pastor, P., Righetti, L., Schaal, S.

In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011), Sept. 25-30, San Francisco, CA, 2011, clmc (inproceedings)

Abstract
Applying model-free reinforcement learning to manipulation remains challeng-ing for several reasons. First, manipulation involves physical contact, which causes discontinuous cost functions. Second, in manipulation, the end-point of the movement must be chosen carefully, as it represents a grasp which must be adapted to the pose and shape of the object. Finally, there is uncertainty in the object pose, and even the most carefully planned movement may fail if the object is not at the expected position. To address these challenges we 1) present a simplified, computationally more ef-ficient version of our model-free reinforcement learning algorithm PI2; 2) extend PI2 so that it simultaneously learns shape parameters and goal parameters of mo-tion primitives; 3) use shape and goal learning to acquire motion primitives that are robust to object pose uncertainty. We evaluate these contributions on a ma-nipulation platform consisting of a 7-DOF arm with a 4-DOF hand.

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link (url) Project Page [BibTex]

link (url) Project Page [BibTex]

2010


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Constrained accelerations for controlled geometric reduction: Sagittal-plane decoupling for bipedal locomotion

Gregg, R. D., Righetti, L., Buchli, J., Schaal, S.

In Humanoid Robots (Humanoids), 2010 10th IEEE-RAS International Conference on, pages: 1-7, December 2010, clmc (inproceedings)

Abstract
Energy-shaping control methods have produced strong theoretical results for asymptotically stable 3D bipedal dynamic walking in the literature. In particular, geometric controlled reduction exploits robot symmetries to control momentum conservation laws that decouple the sagittal-plane dynamics, which are easier to stabilize. However, the associated control laws require high-dimensional matrix inverses multiplied with complicated energy-shaping terms, often making these control theories difficult to apply to highly-redundant humanoid robots. This paper presents a first step towards the application of energy-shaping methods on real robots by casting controlled reduction into a framework of constrained accelerations for inverse dynamics control. By representing momentum conservation laws as constraints in acceleration space, we construct a general expression for desired joint accelerations that render the constraint surface invariant. By appropriately choosing an orthogonal projection, we show that the unconstrained (reduced) dynamics are decoupled from the constrained dynamics. Any acceleration-based controller can then be used to stabilize this planar subsystem, including passivity-based methods. The resulting control law is surprisingly simple and represents a practical way to employ control theoretic stability results in robotic platforms. Simulated walking of a 3D compass-gait biped show correspondence between the new and original controllers, and simulated motions of a 16-DOF humanoid demonstrate the applicability of this method.

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link (url) [BibTex]

2010


link (url) [BibTex]


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Inverse dynamics with optimal distribution of ground reaction forces for legged robots

Righetti, L., Buchli, J., Mistry, M., Schaal, S.

In Proceedings of the international conference on climbing and walking robots (CLAWAR) 2010, Nagoya, Japan, Aug.31-Sept.3, 2010, clmc (inproceedings)

Abstract
The control of the interaction of legged robots with their environment is of crucial importance in the design of locomotion controllers. We need to control the effects of the robots movement on the contact reaction forces to prevent slipping, for example. In this contribution, we extend a recent inverse dynamics algorithm for floating base robots to optimize the distribution of contact forces while achieving precise trajectory tracking. The resulting controller is algorithmically simple as compared to other approaches. Numerical simulations show that this result significantly increases the range of possible movements of a humanoid robot as compared to the previous inverse dynamics algorithm. We also present a simplification of the result for practical use on a real robot. Such an algorithm becomes particularly relevant for agile locomotion of robots on difficult terrains where the contacts with the environment are critical, such as walking over rough or slippery terrain

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link (url) [BibTex]

link (url) [BibTex]

2009


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Modelling the interplay of central pattern generation and sensory feedback in the neuromuscular control of running

Daley, M., Righetti, L., Ijspeert, A.

In Comparative Biochemistry and Physiology - Part A: Molecular & Integrative Physiology. Annual Main Meeting for the Society for Experimental Biology, Glasgow, Scotland, 2009 (inproceedings)

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[BibTex]

2009


[BibTex]


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Compliant Leg Design for a Quadruped Robot

Sproewitz, A., Fremerey, M., Karakasiliotis, K., Rutishauser, S., Righetti, L., Ijspeert, A.

In Proceedings of Dynamic Walking 2009, Vancouver, Canada, 2009 (inproceedings)

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[BibTex]

[BibTex]


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Adaptive Frequency Oscillators and Applications

Righetti, L., Buchli, J., Ijspeert, A.

The Open Cybernetics {\&} Systemics Journal, 3, pages: 64-69, 2009 (article)

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[BibTex]

[BibTex]

2008


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Passive compliant quadruped robot using central pattern generators for locomotion control

Rutishauser, S., Sproewitz, A., Righetti, L., Ijspeert, A.

In 2008 IEEE International Conference on Biomedical Robotics and Biomechatronics, pages: 710-715, 2008 (inproceedings)

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[BibTex]

2008


[BibTex]


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Pattern generators with sensory feedback for the control of quadruped locomotion

Righetti, L., Ijspeert, A.

2008 IEEE International Conference on Robotics and Automation, pages: 819-824, 2008 (proceedings)

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DOI Project Page [BibTex]

DOI Project Page [BibTex]