Estimating the non-linear dynamics of free-flying objects

This paper develops a model-free method to estimate the dynamics of free-flying objects. We take a realistic perspective to the problem and investigate tracking accurately and very rapidly the trajectory and orientation of an object so as to catch it in flight. We consider the dynamics of complex objects where the grasping point is not located at the center of mass. To achieve this, a density estimate of the translational and rotational velocity is built based on the trajectories of various examples. We contrast the performance of six non-linear regression methods (Support Vector Regression (SVR) with Radial Basis Function (RBF) kernel, SVR with polynomial kernel, Gaussian Mixture Regression (GMR), Echo State Network (ESN), Genetic Programming (GP) and Locally Weighted Projection Regression (LWPR)) in terms of precision of recall, computational cost and sensitivity to choice of hyper-parameters. We validate the approach for real-time motion tracking of 5 daily life objects with complex dynamics (a ball, a fully-filled bottle, a half-filled bottle, a hammer and a pingpong racket). To enable real-time tracking, the estimated model of the object's dynamics is coupled with an Extended Kalman Filter for robustness against noisy sensing. (C) 2012 Elsevier B.V. All rights reserved

Path planning with user route preference - A reward surface approximation approach using orthogonal Legendre polynomials

As self driving cars become more ubiquitous, users would look for natural ways of informing the car AI about their personal choice of routes. This choice is not always dictated by straightforward logic such as shortest distance or shortest time, and can be influenced by hidden factors, such as comfort and familiarity. This paper presents a path learning algorithm for such applications, where from limited positive demonstrations, an autonomous agent learns the user's path preference and honors that choice in its route planning, but has the capability to adopt alternate routes, if the original choice(s) become impractical. The learning problem is modeled as a Markov decision process. The states (way-points) and actions (to move from one way-point to another) are pre-defined according to the existing network of paths between the origin and destination and the user's demonstration is assumed to be a sample of the preferred path. The underlying reward function which captures the essence of the demonstration is computed using an inverse reinforcement learning algorithm and from that the entire path mirroring the expert's demonstration is extracted. To alleviate the problem of state space explosion when dealing with a large state space, the reward function is approximated using a set of orthogonal polynomial basis functions with a fixed number of coefficients regardless of the size of the state space. A six fold reduction in total learning time is achieved compared to using simple basis functions, that has dimensionality equal to the number of distinct states

On Blocking Collisions between People, Objects and other Robots

Intentional or unintentional contacts are bound to occur increasingly more often due to the deployment of autonomous systems in human environments. In this paper, we devise methods to computationally predict imminent collisions between objects, robots and people, and use an upper-body humanoid robot to block them if they are likely to happen. We employ statistical methods for effective collision prediction followed by sensor-based trajectory generation and real-time control to attempt to stop the likely collisions using the most favorable part of the blocking robot. We thoroughly investigate collisions in various types of experimental setups involving objects, robots, and people. Overall, the main contribution of this paper is to devise sensor-based prediction, trajectory generation and control processes for highly articulated robots to prevent collisions against people, and conduct numerous experiments to validate this approach

Robotic Ball Catching with an Eye-in-Hand Single-Camera System

In this paper, a unified control framework is proposed to realize a robotic ball catching task with only a moving single-camera (eye-in-hand) system able to catch flying, rolling, and bouncing balls in the same formalism. The thrown ball is visually tracked through a circle detection algorithm. Once the ball is recognized, the camera is forced to follow a baseline in the space so as to acquire an initial dataset of visual measurements. A first estimate of the catching point is initially provided through a linear algorithm. Then, additional visual measurements are acquired to constantly refine the current estimate by exploiting a nonlinear optimization algorithm and a more accurate ballistic model. A classic partitioned visual servoing approach is employed to control the translational and rotational components of the camera differently. Experimental results performed on an industrial robotic system prove the effectiveness of the presented solution. A motion-capture system is employed to validate the proposed estimation process via ground truth

Coordinated multi-arm motion planning: Reaching for moving objects in the face of uncertainty

Sina Mirrazavi Salehian S, Figueroa N, Billard A. Coordinated multi-arm motion planning: Reaching for moving objects in the face of uncertainty. In: Proceedings of Robotics: Science and Systems. AnnArbor, Michigan; 2016