One of the biggest challenges in robotics systems is interacting under uncertainty. Unlike robots, humans learn, adapt and perceive their body as a unity when interacting with the world. We hypothesize that the nervous system counteracts sensor and motor uncertainties by unconscious processes that robustly fuse the available information for approximating their body and the world state. Being able to unite perception and action under a common principle has been sought for decades and active inference is one of the potential unification theories. In this work, we present a humanoid robot interacting with the world by means of a human brain-like inspired perception and control algorithm based on the free-energy principle. Until now, active inference was only tested in simulated examples. Their application on a real robot shows the advantages of such an algorithm for real world applications. The humanoid robot iCub was capable of performing robust reaching behaviors with both arms and active head object tracking in the visual field, despite the visual noise, the artificially introduced noise in the joint encoders (up to 40 degrees deviation), the differences between the model and the real robot and the misdetections of the hand.
A first model of the free-energy optimization in a real robot was performed in @cite_15 working as an approximate Bayesian filter estimation, where the robot was able to perceive its arm location fusing visual, proprioceptive and tactile information. However, authors left out the action. In this work, we took one step further and modelled and applied active inference to the iCub robot for dual arm reaching with active head tracking. For reproducibility, the code is publicly available tobereleased . While the arms goal is to minimize the prediction error between the goal (object) and the end-effector visual location, the head goal is to maintain the object centered in the field of view to provide wider and more accurate reaching capabilities.
@cite_15
2601856331 2472910955 1495441213 2171066085
This paper addresses recursive markerless estimation of a robot's end-effector using visual observations from its cameras. The problem is formulated into the Bayesian framework and addressed using Sequential Monte Carlo (SMC) filtering. We use a 3D rendering engine and Computer Aided Design (CAD) schematics of the robot to virtually create images from the robot's camera viewpoints. These images are then used to extract information and estimate the pose of the end-effector. To this aim, we developed a particle filter for estimating the position and orientation of the robot's end-effector using the Histogram of Oriented Gradient (HOG) descriptors to capture robust characteristic features of shapes in both cameras and rendered images. We implemented the algorithm on the iCub humanoid robot and employed it in a closed-loop reaching scenario. We demonstrate that the tracking is robust to clutter, allows compensating for errors in the robot kinematics and servoing the arm in closed loop using vision. This paper addresses 6-DOF (degree-of-freedom) tactile localization, i.e., the pose estimation of tridimensional objects using tactile measurements. This estimation problem is fundamental for the operation of autonomous robots that are often required to manipulate and grasp objects whose pose is a priori unknown. The nature of tactile measurements, the strict time requirements for real-time operation, and the multimodality of the involved probability distributions pose remarkable challenges and call for advanced nonlinear filtering techniques. Following a Bayesian approach, this paper proposes a novel and effective algorithm, named memory unscented particle filter (MUPF), which solves 6-DOF localization recursively in real time by only exploiting contact point measurements. The MUPF combines a modified particle filter that incorporates a sliding memory of past measurements to better handle multimodal distributions, along with the unscented Kalman filter that moves the particles toward regions of the search space that are more likely with the measurements. The performance of the proposed MUPF algorithm has been assessed both in simulation and on a real robotic system equipped with tactile sensors (i.e., the iCub humanoid robot). The experiments show that the algorithm provides accurate and reliable localization even with a low number of particles and, hence, is compatible with real-time requirements. Minimum time and minimum energy point-to-point trajectories for an industrial robot of the type Manutec r3 are computed subject to state constraints on the angular velocities. The numerical solutions of these optimal control problems are obtained in an efficient way by a combination of a direct collocation and an indirect multiple shooting method. This combination links the benefits of both approaches: A large domain of convergence and a highly accurate solution. The numerical results show that the constraints on the angular velocities become active during large parts of the time optimal motion. But the resulting stress on the links can be significantly reduced by a minimum energy trajectory that is only about ten percent slower than the minimum time trajectory. As a by-product, the reliability of the direct collocation method in estimating adjoint variables and the efficiency of the combination of direct collocation and multiple shooting is demonstrated. The highly accurate solutions reported in this paper may also serve as benchmark problems for other methods. Using visual feedback to control the movement of the end-effector is a common approach for robust execution of robot movements in real-world scenarios. Over the years several visual servoing algorithms have been developed and implemented for various types of robot hardware. In this paper, we present a hybrid approach which combines visual estimations with kinematically determined orientations to control the movement of a humanoid arm. The approach has been evaluated with the humanoid robot ARMAR III using the stereo system of the active head for perception as well as the torso and arms equipped with five finger hands for actuation. We show how a robust visual perception is used to control complex robots without any hand-eye calibration. Furthermore, the robustness of the system is improved by estimating the hand position in case of failed visual hand tracking due to lightning artifacts or occlusions. The proposed control scheme is based on the fusion of the sensor channels for visual perception, force measurement and motor encoder data. The combination of these different data sources results in a reactive, visually guided control that allows the robot ARMAR-III to execute grasping tasks in a real-world scenario.