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
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We describe a learning strategy that allows a humanoid robot to autonomously build a representation of its workspace: we call this representation Reachable Space Map. Interestingly, the robot can use this map to: (i) estimate the Reachability of a visually detected object (i.e. judge whether the object can be reached for, and how well, according to some performance metric) and (ii) modify its body posture or its position with respect to the object to achieve better reaching. The robot learns this map incrementally during the execution of goal-directed reaching movements; reaching control employs kinematic models that are updated online as well. Our solution is innovative with respect to previous works in three aspects: the robot workspace is described using a gaze-centered motor representation, the map is built incrementally during the execution of goal-directed actions, learning is autonomous and online. We implement our strategy on the 48-DOFs humanoid robot Kobian and we show how the Reachable Space Map can support intelligent reaching behavior with the whole-body (i.e. head, eyes, arm, waist, legs). 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 proposes an optimization method for dual-arm robots, inspired by a study on human asymmetric bimanual action called Guiard's principle which states that when humans perform asymmetric bimanual tasks, the right hand (as the lateral preference) performs a fine motion (and force) resolution, while the left hand performs a coarse resolution. To effectively transfer the human bimanual-task knowledge to dual-arm robots, we proposed a cost function, based on task-compatibility index, which is used to set the desired motion and force resolution for each end-effector of the dual-arm according to its role in the bimanual action. Thus the right-arm posture is optimized such that the tool attached to its end-effector can exert fine motion and force. And the left-arm posture is optimized to assume a strong and dynamic structural support for the right arm action. We experimentally compare the proposed cost function against previous methods for dual-arm robots using two six degrees-of-freedom torque-controlled manipulators. The control performance, when mimicking Guiard's principle shows considerably better results. 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. This paper develops an estimation framework for sensor-guided manipulation of a rigid object via a robot arm. Using an unscented Kalman Filter (UKF), the method combines dense range information (from stereo cameras and 3D ranging sensors) as well as visual appearance features and silhouettes of the object and manipulator to track both an object-fixed frame location as well as a manipulator tool or palm frame location. If available, tactile data is also incorporated. By using these different imaging sensors and different imaging properties, we can leverage the advantages of each sensor and each feature type to realize more accurate and robust object and reference frame tracking. The method is demonstrated using the DARPA ARM-S system, consisting of a Barrett™WAM manipulator. Active inference is a general framework for perception and action that is gaining prominence in computational and systems neuroscience but is less known outside these fields. Here, we discuss a proof-of-principle implementation of the active inference scheme for the control or the 7-DoF arm of a (simulated) PR2 robot. By manipulating visual and proprioceptive noise levels, we show under which conditions robot control under the active inference scheme is accurate. Besides accurate control, our analysis of the internal system dynamics (e.g. the dynamics of the hidden states that are inferred during the inference) sheds light on key aspects of the framework such as the quintessentially multimodal nature of control and the differential roles of proprioception and vision. In the discussion, we consider the potential importance of being able to implement active inference in robots. In particular, we briefly review the opportunities for modelling psychophysiological phenomena such as sensory attenuation and related failures of gain control, of the sort seen in Parkinson's disease. We also consider the fundamental difference between active inference and optimal control formulations, showing that in the former the heavy lifting shifts from solving a dynamical inverse problem to creating deep forward or generative models with dynamics, whose attracting sets prescribe desired behaviours. Traditionally, grasp and arm motion planning are considered as separate tasks. This paper presents an integrated approach that only requires the initial configuration of the robotic arm and the pose of the target object to simultaneously plan a good hand pose and arm trajectory to grasp the object. The planner exploits the concept of independent contact regions to look for the best possible grasp. The goal poses for the end effector are obtained using two different methods: one that biases a sampling approach towards favorable regions using principal component analysis, and another one that considers the capabilities of the robotic arm to decide the most promising hand poses. The proposed method is evaluated using different scenarios for the humanoid robot Spacejustin. The predictive functions that permit humans to infer their body state by sensorimotor integration are critical to perform safe interaction in complex environments. These functions are adaptive and robust to non-linear actuators and noisy sensory information. This paper introduces a computational perceptual model based on predictive processing that enables any multisensory robot to learn, infer and update its body configuration when using arbitrary sensors with Gaussian additive noise. The proposed method integrates different sources of information (tactile, visual and proprioceptive) to drive the robot belief to its current body configuration. The motivation is to provide robots with the embodied perception needed for self-calibration and safe physical human-robot interaction. We formulate body learning as obtaining the forward model that encodes the sensor values depending on the body variables, and we solve it by Gaussian process regression. We model body estimation as minimizing the discrepancy between the robot body configuration belief and the observed posterior. We minimize the variational free energy using the sensory prediction errors (sensed vs expected). In order to evaluate the model we test it on a real multi-sensory robotic arm. We show how different sensor modalities contributions, included as additive errors, improve the refinement of the body estimation and how the system adapts itself to provide the most plausible solution even when injecting strong sensory visuo-tactile perturbations. We further analyse the reliability of the model when different sensor modalities are disabled. This provides grounded evidence about the correctness of the perceptual model and shows how the robot estimates and adjusts its body configuration just by means of sensory information. We address self-perception and object discovery by integrating multimodal tactile, proprioceptive and visual cues. Considering sensory signals as the only way to obtain relevant information about the environment, we enable a humanoid robot to infer potential usable objects relating visual self-detection with tactile cues. Hierarchical Bayesian models are combined with signal processing and protoobject artificial attention to tackle the problem. Results show that the robot is able to: (1) discern between inbody and outbody sources without using markers or simplified segmentation; (2) accurately discover objects in the reaching space; and (3) discriminate real objects from visual artefacts, aiding scene understanding. Furthermore, this approach reveals the need for several layers of abstraction for achieving agency and causality due to the inherent ambiguity of the sensory cues. In this work we present a visual servoing approach that enables a humanoid robot to robustly execute dual arm grasping and manipulation tasks. Therefore the target object(s) and both hands are tracked alternately and a combined open- closed-loop controller is used for positioning the hands with respect to the target(s). We address the perception system and how the observable workspace can be increased by using an active vision system on a humanoid head. Furthermore a control framework for reactive positioning of both hands using position based visual servoing is presented, where the sensor data streams coming from the vision system, the joint encoders and the force torque sensors are fused and joint velocity values are generated. This framework can be used for bimanual grasping as well as for two handed manipulations which is demonstrated with the humanoid robot Armar-III that executes grasping and manipulation tasks in a kitchen environment. We propose a control system for humanoid robot reaching using a motor-motor mapping that is learnt online. The system combines endpoint closed-loop and open-loop visual servo control. The closed-loop component moves the eyes, head, arm, and torso, based on the position of the target and the robot’s hand, as seen by the robot’s head mounted cameras. The learned, open-loop component brings the hand into view. Learning from demonstrations has been shown to be a successful method for non-experts to teach manipulation tasks to robots. These methods typically build generative models from demonstrations and then use regression to reproduce skills. However, this approach has limitations to capture hard geometric constraints imposed by the task. On the other hand, while sampling and optimization-based motion planners exist that reason about geometric constraints, these are typically carefully hand-crafted by an expert. To address this technical gap, we contribute with C-LEARN, a method that learns multi-step manipulation tasks from demonstrations as a sequence of keyframes and a set of geometric constraints. The system builds a knowledge base for reaching and grasping objects, which is then leveraged to learn multi-step tasks from a single demonstration. C-LEARN supports multi-step tasks with multiple end effectors; reasons about SE(3) volumetric and CAD constraints, such as the need for two axes to be parallel; and offers a principled way to transfer skills between robots with different kinematics. We embed the execution of the learned tasks within a shared autonomy framework, and evaluate our approach by analyzing the success rate when performing physical tasks with a dual-arm Optimas robot, comparing the contribution of different constraints models, and demonstrating the ability of C-LEARN to transfer learned tasks by performing them with a legged dual-arm Atlas robot in simulation. Humans have at some point learned an abstraction of the capabilities of their arms. By just looking at the scene they can decide which places or objects they can easily reach and which are difficult to approach. Possessing a similar abstraction of a robot arm's capabilities in its workspace is important for grasp planners, path planners and task planners. In this paper, we show that robot arm capabilities manifest themselves as directional structures specific to workspace regions. We introduce a representation scheme that enables to visualize and inspect the directional structures. The directional structures are then captured in the form of a map, which we name the capability map. Using this capability map, a manipulator is able to deduce places that are easy to reach. Furthermore, a manipulator can either transport an object to a place where versatile manipulation is possible or a mobile manipulator or humanoid torso can position itself to enable optimal manipulation of an object. Developmental robotics suggests that the forward and inverse kinematics should be learned through a sensory-motor mapping, instead of being programmed in advance. Motor babbling and goal babbling are two common approaches to generate training samples used to acquire a sensory-motor mapping. Motor babbling typically needs a considerable amount of training data and time to acquire a sufficient mapping, while goal babbling poses difficulties on how to select appropriate goals. In this paper, we propose a neurobiologically-inspired system to progressively learn a sensory-motor mapping bootstrapped from a simple constrained DOF exploration, which generates much less training data than motor babbling. Our proposed system is designed according to two neurobiologically-inspired paradigms: spatiotemporal prediction and uniformity. The spatiotemporal prediction capability facilitates the acquisition of sensory-motor mappings with less amount of training data on the one hand, and facilitates robust behaviour on the other hand. The uniform system design structure is the foundation for building a scalable architecture for cognitive development. We use an improved version of our predictive action selector (PAS) as building block of our system. We validate a PAS on a 2 DOF robot head where the robot learns object tracking and evading. Then we validate a second PAS on a 5 DOF arm where it learns reaching. Humanoid robots are envisioned in general household tasks. To be able to fulfill a given task the robot needs to be equipped with knowledge concerning the manipulation and interaction in the environment and with knowledge about its own capabilities. When performing actions, e.g. opening doors or imitating human reach to grasp movements special 3-d trajectories are followed with the robot's end-effector. These trajectories can not be executed in every part of the robot's arm workspace. Therefore a task planner has to determine if and how additional degrees of freedom such as the robot's upper body or the robot's base can be moved in order to execute the task-specific trajectory. An approach is presented that computes placements for a mobile manipulator online given a task-related 3-d trajectory. A discrete representation of the robot arm's reachable workspace is used. Task-specific trajectories are interpreted as patterns and searched in the reachability model using multi-dimensional correlation. The relevance of the presented approach is demonstrated in simulated positioning tasks. The choice of an adequate object shape representation is critical for efficient grasping and robot manipulation. A good representation has to account for two requirements: it should allow uncertain sensory fusion in a probabilistic way and it should serve as a basis for efficient grasp and motion generation. We consider Gaussian process implicit surface potentials as object shape representations. Sensory observations condition the Gaussian process such that its posterior mean defines an implicit surface which becomes an estimate of the object shape. Uncertain visual, haptic and laser data can equally be fused in the same Gaussian process shape estimate. The resulting implicit surface potential can then be used directly as a basis for a reach and grasp controller, serving as an attractor for the grasp end-effectors and steering the orientation of contact points. Our proposed controller results in a smooth reach and grasp trajectory without strict separation of phases. We validate the shape estimation using Gaussian processes in a simulation on randomly sampled shapes and the grasp controller on a real robot with 7DoF arm and 7DoF hand. We propose the Dexterous Manipulation Graph as a tool to address in-hand manipulation and reposition an object inside a robot's end-effector. This graph is used to plan a sequence of manipulation primitives so to bring the object to the desired end pose. This sequence of primitives is translated into motions of the robot to move the object held by the end-effector. We use a dual arm robot with parallel grippers to test our method on a real system and show successful planning and execution of in-hand manipulation. Perceiving accurate 3D object shape is important for robots to interact with the physical world. Current research along this direction has been primarily relying on visual observations. Vision, however useful, has inherent limitations due to occlusions and the 2D-3D ambiguities, especially for perception with a monocular camera. In contrast, touch gets precise local shape information, though its efficiency for reconstructing the entire shape could be low. In this paper, we propose a novel paradigm that efficiently perceives accurate 3D object shape by incorporating visual and tactile observations, as well as prior knowledge of common object shapes learned from large-scale shape repositories. We use vision first, applying neural networks with learned shape priors to predict an object's 3D shape from a single-view color image. We then use tactile sensing to refine the shape; the robot actively touches the object regions where the visual prediction has high uncertainty. Our method efficiently builds the 3D shape of common objects from a color image and a small number of tactile explorations (around 10). Our setup is easy to apply and has potentials to help robots better perform grasping or manipulation tasks on real-world objects. The majority of existing pedestrian trackers concentrate on maintaining the identities of targets, however systems for remote biometric analysis or activity recognition in surveillance video often require stable bounding-boxes around pedestrians rather than approximate locations. We present a multi-target tracking system that is designed specifically for the provision of stable and accurate head location estimates. By performing data association over a sliding window of frames, we are able to correct many data association errors and fill in gaps where observations are missed. The approach is multi-threaded and combines asynchronous HOG detections with simultaneous KLT tracking and Markov-Chain Monte-Carlo Data Association (MCM-CDA) to provide guaranteed real-time tracking in high definition video. Where previous approaches have used ad-hoc models for data association, we use a more principled approach based on a Minimal Description Length (MDL) objective which accurately models the affinity between observations. We demonstrate by qualitative and quantitative evaluation that the system is capable of providing precise location estimates for large crowds of pedestrians in real-time. To facilitate future performance comparisons, we make a new dataset with hand annotated ground truth head locations publicly available. Coordinated control strategies for multi-robot systems are necessary for tasks that cannot be executed by a single robot. This encompasses tasks where the workspace of the robot is too small or where the load is too heavy for one robot to handle. Using multiple robots makes the task feasible by extending the workspace and or increase the payload of the overall robotic system. In this paper, we consider two instances of such task: a co-worker scenario in which a human hands over a large object to a robot; intercepting a large flying object. The problem is made difficult as the pick-up intercept motions must take place while the object is in motion and because the object's motion is not deterministic. The challenge is then to adapt the motion of the robotic arms in coordination with one another and with the object. Determining the pick-up intercept point is done by taking into account the workspace of the multi-arm system and is continuously recomputed to adapt to change in the object's trajectory. We propose a dynamical systems (DS) based control law to generate autonomous and synchronized motions for a multi-arm robot system in the task of reaching for a moving object. We show theoretically that the resulting DS coordinates the motion of the robots with each other and with the object, while the system remains stable. We validate our approach on a dual-arm robotic system and demonstrate that it can re-synchronize and adapt the motion of each arm in synchrony in a fraction of seconds, even when the motion of the object is fast and not accurately predictable.