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2017


Scalable Pneumatic and Tendon Driven Robotic Joint Inspired by Jumping Spiders
Scalable Pneumatic and Tendon Driven Robotic Joint Inspired by Jumping Spiders

Sproewitz, A., Göttler, C., Sinha, A., Caer, C., Öztekin, M. U., Petersen, K., Sitti, M.

In Proceedings 2017 IEEE International Conference on Robotics and Automation (ICRA), pages: 64-70, IEEE, Piscataway, NJ, USA, IEEE International Conference on Robotics and Automation (ICRA), May 2017 (inproceedings)

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Video link (url) DOI Project Page [BibTex]

2017


Video link (url) DOI Project Page [BibTex]


Linking {Mechanics} and {Learning}
Linking Mechanics and Learning

Heim, S., Grimminger, F., Drama, Ö., Spröwitz, A.

In Proceedings of Dynamic Walking 2017, 2017 (inproceedings)

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[BibTex]

[BibTex]


Is Growing Good for Learning?
Is Growing Good for Learning?

Heim, S., Spröwitz, A.

Proceedings of the 8th International Symposium on Adaptive Motion of Animals and Machines AMAM2017, 2017 (conference)

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[BibTex]

[BibTex]


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Momentum-Centered Control of Contact Interactions

Righetti, L., Herzog, A.

In Geometric and Numerical Foundations of Movements, 117, pages: 339-359, Springer Tracts in Advanced Robotics, Springer, Cham, 2017 (incollection)

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link (url) [BibTex]

link (url) [BibTex]


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Pattern Generation for Walking on Slippery Terrains

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

In 2017 5th International Conference on Robotics and Mechatronics (ICROM), Iran, August 2017 (inproceedings)

Abstract
In this paper, we extend state of the art Model Predictive Control (MPC) approaches to generate safe bipedal walking on slippery surfaces. In this setting, we formulate walking as a trade off between realizing a desired walking velocity and preserving robust foot-ground contact. Exploiting this for- mulation inside MPC, we show that safe walking on various flat terrains can be achieved by compromising three main attributes, i. e. walking velocity tracking, the Zero Moment Point (ZMP) modulation, and the Required Coefficient of Friction (RCoF) regulation. Simulation results show that increasing the walking velocity increases the possibility of slippage, while reducing the slippage possibility conflicts with reducing the tip-over possibility of the contact and vice versa.

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link (url) [BibTex]

link (url) [BibTex]

2015


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Trajectory generation for multi-contact momentum control

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

In 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids), pages: 874-880, IEEE, Seoul, South Korea, 2015 (inproceedings)

Abstract
Simplified models of the dynamics such as the linear inverted pendulum model (LIPM) have proven to perform well for biped walking on flat ground. However, for more complex tasks the assumptions of these models can become limiting. For example, the LIPM does not allow for the control of contact forces independently, is limited to co-planar contacts and assumes that the angular momentum is zero. In this paper, we propose to use the full momentum equations of a humanoid robot in a trajectory optimization framework to plan its center of mass, linear and angular momentum trajectories. The model also allows for planning desired contact forces for each end-effector in arbitrary contact locations. We extend our previous results on linear quadratic regulator (LQR) design for momentum control by computing the (linearized) optimal momentum feedback law in a receding horizon fashion. The resulting desired momentum and the associated feedback law are then used in a hierarchical whole body control approach. Simulation experiments show that the approach is computationally fast and is able to generate plans for locomotion on complex terrains while demonstrating good tracking performance for the full humanoid control.

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link (url) DOI [BibTex]

2015


link (url) DOI [BibTex]


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Humanoid Momentum Estimation Using Sensed Contact Wrenches

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

In 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids), pages: 556-563, IEEE, Seoul, South Korea, 2015 (inproceedings)

Abstract
This work presents approaches for the estimation of quantities important for the control of the momentum of a humanoid robot. In contrast to previous approaches which use simplified models such as the Linear Inverted Pendulum Model, we present estimators based on the momentum dynamics of the robot. By using this simple yet dynamically-consistent model, we avoid the issues of using simplified models for estimation. We develop an estimator for the center of mass and full momentum which can be reformulated to estimate center of mass offsets as well as external wrenches applied to the robot. The observability of these estimators is investigated and their performance is evaluated in comparison to previous approaches.

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link (url) DOI [BibTex]

link (url) DOI [BibTex]

2013


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

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link (url) DOI [BibTex]

2013


link (url) DOI [BibTex]


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Learning Objective Functions for Manipulation

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

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

Abstract
We present an approach to learning objective functions for robotic manipulation based on inverse reinforcement learning. Our path integral inverse reinforcement learning algorithm can deal with high-dimensional continuous state-action spaces, and only requires local optimality of demonstrated trajectories. We use L 1 regularization in order to achieve feature selection, and propose an efficient algorithm to minimize the resulting 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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link (url) DOI [BibTex]

link (url) DOI [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, 57, pages: 35-51, Lecture Notes in Electrical Engineering, Springer Berlin Heidelberg, 2013 (incollection)

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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link (url) [BibTex]

link (url) [BibTex]


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Learning Task Error Models for Manipulation

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

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

Abstract
Precise kinematic forward models are important for robots to successfully perform dexterous grasping and manipulation 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 counterbalancing 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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link (url) DOI [BibTex]

link (url) DOI [BibTex]

2006


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Movement generation using dynamical systems : a humanoid robot performing a drumming task

Degallier, S., Santos, C. P., Righetti, L., Ijspeert, A.

In 2006 6th IEEE-RAS International Conference on Humanoid Robots, pages: 512-517, IEEE, Genova, Italy, 2006 (inproceedings)

Abstract
The online generation of trajectories in humanoid robots remains a difficult problem. In this contribution, we present a system that allows the superposition, and the switch between, discrete and rhythmic movements. Our approach uses nonlinear dynamical systems for generating trajectories online and in real time. Our goal is to make use of attractor properties of dynamical systems in order to provide robustness against small perturbations and to enable online modulation of the trajectories. The system is demonstrated on a humanoid robot performing a drumming task.

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link (url) DOI [BibTex]

2006


link (url) DOI [BibTex]


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Design methodologies for central pattern generators: an application to crawling humanoids

Righetti, L., Ijspeert, A.

In Proceedings of Robotics: Science and Systems, Philadelphia, USA, August 2006 (inproceedings)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Programmable central pattern generators: an application to biped locomotion control

Righetti, L., Ijspeert, A.

In Proceedings of the IEEE International Conference on Robotics and Automation, 2006. ICRA 2006., pages: 1585-1590, IEEE, 2006 (inproceedings)

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[BibTex]

[BibTex]

2003


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Evolution of Fault-tolerant Self-replicating Structures

Righetti, L., Shokur, S., Capcarre, M.

In Advances in Artificial Life, pages: 278-288, Lecture Notes in Computer Science, Springer Berlin Heidelberg, 2003 (inproceedings)

Abstract
Designed and evolved self-replicating structures in cellular automata have been extensively studied in the past as models of Artificial Life. However, CAs, unlike their biological counterpart, are very brittle: any faulty cell usually leads to the complete destruction of any emerging structures, let alone self-replicating structures. A way to design fault-tolerant structures based on error-correcting-code has been presented recently [1], but it required a cumbersome work to be put into practice. In this paper, we get back to the original inspiration for these works, nature, and propose a way to evolve self-replicating structures, faults here being only an idiosyncracy of the environment.

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link (url) DOI [BibTex]

2003


link (url) DOI [BibTex]