Beyond Basins of Attraction: Quantifying Robustness of Natural Dynamics

Properly designing a system to exhibit favorable natural dynamics can greatly simplify designing or learning the control policy. However, it is still unclear what constitutes favorable natural dynamics and how to quantify its effect. Most studies of simple walking and running models have focused on the basins of attraction of passive limit-cycles and the notion of self-stability. We instead emphasize the importance of stepping beyond basins of attraction. We show an approach based on viability theory to quantify robust sets in state-action space. These sets are valid for the family of all robust control policies, which allows us to quantify the robustness inherent to the natural dynamics before designing the control policy or specifying a control objective. We illustrate our formulation using spring-mass models, simple low dimensional models of running systems. We then show an example application by optimizing robustness of a simulated planar monoped, using a gradient-free optimization scheme. Both case studies result in a nonlinear effective stiffness providing more robustness.Comment: 15 pages. This work has been accepted to IEEE Transactions on Robotics (2019

Fault diagnosis for vehicle lateral dynamics with robust threshold

This paper investigates the robust fault diagnosis problem for vehicle lateral dynamics, which play a key role in vehicle stability and driving safety. The proposed fault diagnosis system consists of two sub-systems: fault diagnosis observer and robust threshold. By treating faults as disturbances, Disturbance/Uncertainty Estimation technique is used as fault diagnosis observer to generate residuals. Considering that residuals of model-based fault diagnosis are subject to the effect of uncertainties and consequently large false alarm rate may be resulted in, a novel robust threshold is then proposed based on reachability analysis technique for uncertain systems. The proposed fault diagnosis system is finally applied to the accelerometer and gyrometer sensor fault diagnosis problem of vehicle lateral dynamics, where initial states and velocity are considered to be uncertain. Simulation study verifies the effectiveness of the proposed fault diagnosis system

Learning from Outside the Viability Kernel: Why we Should Build Robots that can Fall with Grace

Despite impressive results using reinforcement learning to solve complex problems from scratch, in robotics this has still been largely limited to model-based learning with very informative reward functions. One of the major challenges is that the reward landscape often has large patches with no gradient, making it difficult to sample gradients effectively. We show here that the robot state-initialization can have a more important effect on the reward landscape than is generally expected. In particular, we show the counter-intuitive benefit of including initializations that are unviable, in other words initializing in states that are doomed to fail.Comment: Proceedings of the 2018 IEEE International Conference on SImulation, Modeling and Programming for Autonomous Robots (SIMPAR), Brisbane, Australia, 16-19 201

Data-Driven Grasp Synthesis - A Survey

We review the work on data-driven grasp synthesis and the methodologies for sampling and ranking candidate grasps. We divide the approaches into three groups based on whether they synthesize grasps for known, familiar or unknown objects. This structure allows us to identify common object representations and perceptual processes that facilitate the employed data-driven grasp synthesis technique. In the case of known objects, we concentrate on the approaches that are based on object recognition and pose estimation. In the case of familiar objects, the techniques use some form of a similarity matching to a set of previously encountered objects. Finally for the approaches dealing with unknown objects, the core part is the extraction of specific features that are indicative of good grasps. Our survey provides an overview of the different methodologies and discusses open problems in the area of robot grasping. We also draw a parallel to the classical approaches that rely on analytic formulations.Comment: 20 pages, 30 Figures, submitted to IEEE Transactions on Robotic