Bruno D. Damas

A Modified Potential Fields Method for Robot Navigation Applied to Dribbling in Robotic Soccer

This paper describes a modified potential fields method for robot navigation, especially suited for unicycle-type non-holonomic mobile robots. The potential field is modified so as to enhance the relevance of obstacles in the direction of the robot motion. The relative weight assigned to front and side obstacles can be modified by the adjustment of one physically interpretable parameter. The resulting angular speed and linear acceleration of the robot can be expressed as functions of the linear speed, distance and relative orientation to the obstacles. For soccer robots, moving to a desired posture with and without the ball are relevant issues. To enable a soccer robot to dribble a ball, i.e., to move while avoiding obstacles and pushing the ball without losing it, under severe restrictions to ball holding capabilities, a further constraint among the angular speed, linear speed and linear acceleration is introduced. This dribbling behavior has been used successfully in the robots of the RoboCup Middle-Size League ISocRob team.

Avoiding moving obstacles: the forbidden velocity map

Robotic obstacle avoidance in cluttered and dense environments is an important issue in robotic navigation. Over the past few years a number of techniques has been proposed to deal with safe navigation among obstacles in unknown scenarios. Unfortunately many of these methods do not consider obstacle velocities, which can rise some serious questions concerning their safety. This paper will deal with a novel approach to moving obstacle avoidance in holonomic robots. It proposes the Forbidden Velocity Map, a generalization of the Dynamic Window concept that considers obstacle and robot shape, velocity and dynamics, resulting in a safe, reactive real-time navigation algorithm that is able to deal with navigation in unpredictable and cluttered scenarios.

Stochastic discrete event model of a multi-robot team playing an adversarial game

Abstract This paper introduces a method to model multi-robot teams using stochastic discrete event system techniques. The environment state space and robot behaviours are discretised and modelled by modular finite state automata (FSA). Then, all the FSA are composed to obtain the complete model of the team situated in its environment. Controllable and uncontrollable events are identified. Exponential distributions are assigned to the interevent times for uncontrollable events and stochastic dynamic programming is applied to the optimal selection of the controllable events. The method is illustrated by its application to a robotic football game. Simulation results are presented.

An online algorithm for simultaneously learning forward and inverse kinematics

This paper proposes a supervised algorithm for online learning of input-output relations that is particularly suitable to simultaneously learn the forward and inverse kinematics of general manipulators - the multi-valued nature of the inverse kinematics of serial chains and forward kinematics of parallel manipulators makes it infeasible to apply state-of-the-art learning techniques to these problems, as they typically assume a single-valued function to be learned. The proposed algorithm is based on a generalized expectation maximization approach to fit an infinite mixture of linear experts to an online stream of data samples, together with an outlier probabilistic model that dynamically grows the number of linear experts allocated to the mixture, this way controlling the complexity of the resulting model. The result is an incremental, online and localized learning algorithm that performs nonlinear, multivariate regression on multivariate outputs by approximating the target function by a linear relation within each expert input domain, which can directly provide forward and inverse multi-valued estimates. The experiments presented in this paper show that it can achieve, for single-valued functions, a performance directly comparable to state-of-the-art online function approximation algorithms, while additionally providing inverse predictions and the capability to learn multi-valued functions in a natural manner. To our knowledge this is a distinctive property of the algorithm presented in this paper.

Incremental development of multiple tool models for robotic reaching through autonomous exploration

Autonomy and flexibility are two major requirements for modern robots. In particular, humanoid robots should learn new skills incrementally through autonomous exploration, and adapt to different contexts. In this paper we consider the problem of learning forward models for task space control under dynamically varying kinematic contexts: the robot learns incrementally and autonomously its forward kinematics under different contexts, represented by the inclusion of different tools, and exploits the learned model to realize reaching with those tools. We model the forward kinematics as a multi-valued function, in which different outputs for the same input query are related to different tools (i.e. contexts). The model is estimated using IMLE, a recent online learning algorithm for multi-valued regression, and used for control. No information is given about the tool changes, nor any assumption is made about the tool kinematics. Results are provided both in simulation and with a full-body humanoid. In the latter case we show how the robot successfully performs reaching using a flexible tool, a clear example of complex kinematics.

A learning framework for generic sensory-motor maps

We present a new approach to cope with unknown redundant systems. For this we present i) an online algorithm that learns general input-output restrictions and, ii) a method that, given a partial set of input-output variables, provides an estimate of the remaining ones, using the learned restrictions. We show applications of the algorithm using examples of direct and inverse robot kinematics.

Online learning of humanoid robot kinematics under switching tools contexts

In this paper a novel approach to kinematics learning and task space control, under switching contexts, is presented. Such non-stationary contexts may appear in many robotic tasks: in particular, the changing of the context due to the use of tools with different lengths and shapes is herein studied. We model the robot forward kinematics as a multi-valued function, in which different outputs for the same input query are related to actual different hidden contexts. To do that, we employ IMLE, a recent online learning algorithm that fits an infinite mixture of linear experts to the online stream of training data. This algorithm can directly provide multi-valued regression in a online fashion, while having, for classic single-valued regression, a performance comparable to state-of-the-art online learning algorithms. The context varying forward kinematics is learned online through exploration, not relying on any kind of prior knowledge. Using the proposed approach, the robot can dynamically learn how to use different tools, without forgetting the kinematic mappings concerning previously manipulated tools. No information is given about such tool changes to the learning algorithm, nor any assumption is made about the tool kinematics. To our knowledge this is the most general and efficient approach to learning and control under discrete varying contexts. Some experimental results obtained on a high-dimensional simulated humanoid robot provide a strong support to our approach.

Open and closed-loop task space trajectory control of redundant robots using learned models

This paper presents a comparison of open-loop and closed-loop control strategies for tracking a task space trajectory, using redundant robots. We do not assume any knowledge of the analytical forward and inverse kinematics, relying instead on learning these models online, while executing a desired task. Specifically, we employ a recent learning algorithm that allows to learn a probabilistic model from which both the forward and inverse solutions can be obtained, as well as the Jacobian of the kinematics map. Such learned model can then be used to implement both types of control. Moreover, the multi-valued solutions provided by the learned model can be applied to redundant systems in which an infinite number of inverse solutions may exist. We present experiments with a simulated version of the iCub, a highly redundant humanoid robot, in which this learned model is employed to execute both open-loop and closed-loop trajectory control. We show the advantages and drawbacks of both control strategies, and we propose a way to combine them to deal with sensor noise and failures, showing the benefits of using a learning algorithm that can simultaneously provide forward and inverse predictions.

Incremental learning of context-dependent dynamic internal models for robot control

Accurate dynamic models can be very difficult to compute analytically for complex robots; moreover, using a precomputed fixed model does not allow to cope with unexpected changes in the system. An interesting alternative solution is to learn such models from data, and keep them up-to-date through online adaptation. In this paper we consider the problem of learning the robot inverse dynamic model under dynamically varying contexts: the robot learns incrementally and autonomously the model under different conditions, represented by the manipulation of objects of different weights, that change the dynamics of the system. The inverse dynamic mapping is modeled as a multi-valued function, in which different outputs for the same input query are related to different dynamic contexts (i.e. different manipulated objects). The mapping is estimated using IMLE, a recent online learning algorithm for multi-valued regression, and used for Computed Torque control. No information is given about the context switch during either learning or control, nor any assumption is made about the kind of variation in the dynamics imposed by a new contexts. Experimental results with the iCub humanoid robot are provided.

Online learning of single-and multivalued functions with an infinite mixture of linear experts

We present a supervised learning algorithm for estimation of generic input-output relations in a real-time, online fashion. The proposed method is based on a generalized expectation-maximization approach to fit an infinite mixture of linear experts (IMLE) to an online stream of data samples. This probabilistic model, while not fully Bayesian, can efficiently choose the number of experts that are allocated to the mixture, this way effectively controlling the complexity of the resulting model. The result is an incremental, online, and localized learning algorithm that performs nonlinear, multivariate regression on multivariate outputs by approximating the target function by a linear relation within each expert input domain and that can allocate new experts as needed. A distinctive feature of the proposed method is the ability to learn multivalued functions: one-to-many mappings that naturally arise in some robotic and computer vision learning domains, using an approach based on a Bayesian generative model for the predictions provided by each of the mixture experts. As a consequence, it is able to directly provide forward and inverse relations from the same learned mixture model. We conduct an extensive set of experiments to evaluate the proposed algorithm performance, and the results show that it can outperform state-of-the-art online function approximation algorithms in single-valued regression, while demonstrating good estimation capabilities in a multivalued function approximation context.