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.
Active inference (under the free-energy principle) includes action as a classical spinal reflex arc pathway triggered by perception prediction errors and has been mainly studied in theoretical or simulated conditions. Friston presented in @cite_8 a theoretical motor model with two degrees of freedom as an extension of the dynamic expectation maximization algorithm. It was recently studied in robot control of a simulated PR2 robot @cite_11 , one degree of freedom simulated vehicle @cite_10 and two degrees of freedom simulated robot arm @cite_2 .
@cite_2 @cite_11 @cite_10 @cite_8
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When a mobile manipulator functions as an assistive device, the robot's initial configuration and the configuration of the environment can impact the robot's ability to provide effective assistance. Selecting initial configurations for assistive tasks can be challenging due to the high number of degrees of freedom of the robot, the environment, and the person, as well as the complexity of the task. In addition, rapid selection of initial conditions can be important, so that the system will be responsive to the user and will not require the user to wait a long time while the robot makes a decision. To address these challenges, we present Task-centric initial Configuration Selection (TCS), which unlike previous work uses a measure of task-centric manipulability to accommodate state estimation error, considers various environmental degrees of freedom, and can find a set of configurations from which a robot can perform a task. TCS performs substantial offline computation, so that it can rapidly provide solutions at run time. At run time, the system performs an optimization over candidate initial configurations using a utility function that can include factors such as movement costs for the robot's mobile base. To evaluate TCS, we created models of 11 activities of daily living (ADLs) and evaluated TCS's performance with these 11 assistive tasks in a computer simulation of a PR2, a robotic bed, and a model of a human body. TCS performed as well or better than a baseline algorithm in all of our tests against state estimation error. Advanced modern humanoid robots often have complex manipulators with a large number of degrees of freedom. Thus, motion planning for such manipulators is a very computationally challenging problem. However, often robotic manipulators allow the wrist degrees of freedom to be controlled independently from the configuration of the rest of the arm. In this paper we show how to split the high dimensional planning problem into two lower-dimensional sub-problemsplanning for the main arm joints and planning for the wrist joints, without losing guarantees on completeness. This approach is an extension of our previously developed framework for planning with adaptive dimensionality. Experimentally, we show that this approach is very effective in speeding up planning for robotic arms on Willow Garages PR2 platform. We compare our algorithm with several popular alternative approaches for performing motion planning for robotic arms. The results we observe illustrate that our algorithm provides a good balance between planning time, planning success rate, path consistency, and path quality. In this paper, we present a robotic model-based reinforcement learning method that combines ideas from model identification and model predictive control. We use a feature-based representation of the dynamics that allows the dynamics model to be fitted with a simple least squares procedure, and the features are identified from a high-level specification of the robot's morphology, consisting of the number and connectivity structure of its links. Model predictive control is then used to choose the actions under an optimistic model of the dynamics, which produces an efficient and goal-directed exploration strategy. We present real time experimental results on standard benchmark problems involving the pendulum, cartpole, and double pendulum systems. Experiments indicate that our method is able to learn a range of benchmark tasks substantially faster than the previous best methods. To evaluate our approach on a realistic robotic control task, we also demonstrate real time control of a simulated 7 degree of freedom arm. Remote control robots are being found in an increasing number of application domains, including search and rescue, exploration, and reconnaissance. There is a large body of HRI research that investigates interface design for remote navigation, control, and sensor monitoring, while aiming for interface enhancements that benefit the remote operator such as improving ease of use, reducing operator mental load, and maximizing awareness of a robot's state and remote environment. Even though many remote control robots have multi-degree-of-freedom robotic manipulator arms for interacting with the environment, there is only limited research into easy-to-use remote control interfaces for such manipulators, and many commercial robotic products are still using simplistic interface technologies such as keypads or gamepads with arbitrary mappings to arm morphology. In this paper, we present an original interface for the remote control of a multi-degree of freedom robotic arm. We conducted a controlled experiment to compare our interface to an existing commercial keypad interface and detail our results that indicate our interface was easier to use, required less cognitive task load, and enabled people to complete tasks more quickly. In this paper, we present an original interface for the remote control of a multi-degree of freedom robotic arm. We conducted a controlled experiment to compare our interface to an existing commercial keypad interface and detail our results that indicate our interface was easier to use, required less cognitive task load, and enabled people to complete tasks more quickly.