Learning From Demonstration and Interactive Control of Variable-Impedance to Cut Soft Tissues

In this article, we propose an approach to extract variable-impedance during cutting tasks from human demonstrations, so as to ease soft-tissue cutting by robots. We model the dynamic adjustment of the human arm during interactions with the tissue and transfer these adaptive capabilities to the robot, by learning both the motion and change of impedance. To improve performance during task execution, our variable-impedance skill-transfer framework combines the learned model with an interactive-operation and feedback controller. To offer the flexibility of modifying the trajectory at run time, we use a control law based on dynamical systems. We couple this control law with virtual dynamics that describes the cutting dynamics. This ensures that the robot can control the interaction force and can plan the trajectory. The approach is validated in a real robot cutting-experiment of cutting different hardness tissues. Results show that our learning-based feedback controller, with the assistance from interactive-operation models, can effectively improve the task’s success rate, cutting length, cutting depth, and other indicators, as compared to using a fixed and variable-impedance gain controller.

Control and Learning of Compliant Manipulation Skills

Klas Jonas Alfred Kronander

Humans demonstrate an impressive capability to manipulate fragile objects without damaging them, graciously controlling the force and position of hands or tools. Traditionally, robotics has favored position control over force control to produce fast, accurate and repeatable motion. For extending the applicability of robotic manipulators outside the strictly controlled environments of industrial work cells, position control is inadequate. Tasks that involve contact with objects whose positions are not known with perfect certainty require a controller that regulates the relationship between positional deviations and forces on the robot. This problem is formalized in the impedance control framework, which focuses the robot control problem on the interaction between the robot and its environment. By adjusting the impedance parameters, the behavior of the robot can be adapted to the need of the task. However, it is often difficult to specify formally how the impedance should vary for best performance. Furthermore, fast it can be shown that careless variation of the impedance can lead to unstable regulation or tracking even in free motion. In the first part of the thesis, the problem of how to define a varying impedance for a task is addressed. A haptic human-robot interface that allows a human supervisor to teach impedance variations by physically interacting with the robot during task execution is introduced. It is shown that the interface can be used to enhance the performance in several manipulation tasks. Then, the problem of stable control with varying impedance is addressed. Along with a theoretical discussion on this topic, a sufficient condition for stable varying stiffness and damping is provided. In the second part of the thesis, we explore more complex manipulation scenarios via online generation of the robot trajectory. This is done along two axes 1) learning how to react to contact forces in insertion tasks which are crucial for assembly operations and 2) autonomous Dynamical Systems (DS) for motion representation with the capability to encode a family of trajectories rather than a fixed, time-dependent reference. A novel framework for task representation using DS is introduced, termed Locally Modulated Dynamical Systems (LMDS). LMDS differs from existing DS estimation algorithms in that it supports non-parametric and incremental learning all the while guaranteeing that the resulting DS is globally stable at an attractor point. To combine the advantages of DS motion generation with impedance control, a novel controller for tasks described by first order DS is proposed. The controller is passive, and has the properties of an impedance controller with the added flexibility of a DS motion representation instead of a time-indexed trajectory.

EPFL2015

Towards Dynamic Trot Gait Locomotion—Design, Control and Experiments with Cheetah-cub, a Compliant Quadruped Robot

Mostafa Ajallooeian, Auke Ijspeert, Alexandre Tuleu, Massimo Vespignani

We present robot design and results from locomotion experiments with a novel, compliant quadruped robot: Cheetah-cub. The robot's leg configuration is based on a spring-loaded, panthograph-mechanism with multiple segments. A dedicated open-loop, joint-space locomotion controller was derived and implemented. Experimentally, we found high speed and dynamic gaits, and evaluated the robot's locomotion characteristics. Experiments were run in simulation and in hardware on flat terrain, and at a step-down obstacle. The robot reached a running trot with maximum speed of 1.42m/s, in the hardware experiments. This speed corresponds to a Froude number of 1.3, or 6.9 body lengths per second. Besides typical control parameters, also morphological parameters such as the leg design played a role for maximum robot speed. Our robot platform has several advantages, especially when compared to larger and stiffer quadruped robot designs. 1) It is the fastest (Froude number) trotting quadruped robot of its kind, light-weight, compact, electrically powered, and made from many off-the-shelf components. 2) The robot shows self-stabilizing behavior at increasing robot speed, with open-loop control. 3) It is cheap, easy to reproduce, robust, and safe to handle. This makes it an excellent tool for research of multi-segment legs in quadruped robots.

2013

A Control and Optimization Framework for Robot Locomotion

Soha Pouya

Despite great efforts in designing legged robots, we are still far from the adaptivity, efficiency and robustness with which animals can move. Observations from successful biomimetic designs highlight the significant role of clever morphological design in achieving such performance. In addition, the important role of the controller in improving the locomotion performance is undoubtable. In fact, there is a complex link between morphology and motor control of locomoting systems. In animals’ locomotion, there is a large number of evidence showing the effective link between their morphology and functionality. Motivated by biological observations, similar questions can be posed from a robotics perspective: “what are the relative and complementary roles of robot control and morphology (similar to animal’s brain and body) in the generated locomotor behavior?”, and more pragmatically, “how can we take lessons from these observations to reduce the current gap between robots’ locomotion performance and what is observed in their biological counterparts?”. These are the fundamental questions that have motivated studies in this thesis. While empirical studies are essential to address aforementioned questions, they require simplifying contexts to yield insightful predictions and examinable theories. Both theoretical and physical models (i.e. simulations and robots) can significantly contribute in providing such insights. While robot experiments are essential to evaluate and explore the involved biological concept, they are usually time-consuming and costly. Hence, it would be advantageous if one could exploit the power of theoretical models for development of ideas and hypotheses before their implementation on the real robot. In this thesis, we aim at leveraging the strength of theoretical models to study the above-mentioned questions, namely, the relative role of control and morphology in improving the robot locomotion functionalities. To provide useful theoretical models, we take a step-wise approach, where we start from model-free methods and proceed with advancing the locomotion control by incorporating dynamics models at different levels of complexity. In particular, we develop a pool of robot models, starting from a simplified massless leg to a detailed dynamics model of the leg, to a 2D quadruped with spine joint and eventually to a 3D detailed dynamics model of a quadruped. Along with developing dynamics model of the robot, we devise a set of control and optimization techniques with the main goal of improving locomotion performance. There are several performance criteria to consider (such as energy efficiency, maneuverability, adaptivity to unknown terrains) and a key question is how to build the control architecture to hold a proper trade-off between these partially contradicting criteria. We take advantage of the developed dynamic models in both (i) improving the controller performance and (ii) implementing analytical tools to evaluate and compare different locomotion performance criteria. The central outcome of this thesis is a bio-inspired modeling, control, and optimization framework for legged robot locomotion, which is evaluated on a set of robots with different morphologies. In all cases, we explore how the locomotion performance can be improved by introducing new control techniques or be influenced by the morphological aspects (such as joint configuration and mass distribution). In particular, we show the effect of using more detailed dynamics model to improve the predictive performance of the simulations both for analyzing morphological factors and for robustness of the controller to internal (e.g. change in the inertial parameters) and external (e.g. walking on rough terrains) perturbations. The framework is generic and is used to model, control and analyze locomotion gaits for three compliant quadruped robots (Locomorph, Cheetah-Cub and Bobcat robots) and a modular robot (Roombots).