Active Inference and Behavior Trees for Reactive Action Planning and Execution in Robotics

In this article, we propose a hybrid combination of active inference and behavior trees (BTs) for reactive action planning and execution in dynamic environments, showing how robotic tasks can be formulated as a free-energy minimization problem. The proposed approach allows handling partially observable initial states and improves the...

Dynamic Optimization Fabrics for Motion Generation

Optimization fabrics are a geometric approach to real-time local motion generation, where motions are designed by the composition of several differential equations that exhibit a desired motion behavior. We generalize this framework to dynamic scenarios and nonholonomic robots and prove that fundamental properties can be conserved. We show...

Free Energy Principle for State and Input Estimation of a Quadcopter Flying in Wind

The free energy principle from neuroscience provides a brain-inspired perception scheme through a data-driven model learning algorithm called Dynamic Expectation Maximization (DEM). This paper aims at introducing an exper-imental design to provide the first experimental confirmation of the usefulness of DEM as a state and input estimator for...

Dynamic Expectation Maximization Algorithm for Estimation of Linear Systems with Colored Noise

The free energy principle from neuroscience has recently gained traction as one of the most prominent brain theories that can emulate the brain’s perception and action in a bio-inspired manner. This renders the theory with the potential to hold the key for general artificial intelligence. Leveraging this potential, this paper aims to bridge the...

Active inference for fault tolerant control of robot manipulators with sensory faults

We present a fault tolerant control scheme for robot manipulators based on active inference. The proposed solution makes use of the sensory prediction errors in the free-energy to simplify the residuals and thresholds generation for fault detection and isolation and does not require additional controllers for fault recovery. Results...

A novel algorithm for fast grasping of unknown objects using C-shape configuration

Increasing grasping efficiency is very important for the robots to grasp unknown objects especially subjected to unfamiliar environments. To achieve this, a new algorithm is proposed based on the C-shape configuration. Specifically, the geometric model of the used under-actuated gripper is approximated as a C-shape. To obtain an appropriate...

Viewpoint optimization for aiding grasp synthesis algorithms using reinforcement learning

Grasp synthesis for unknown objects is a challenging problem as the algorithms are expected to cope with missing object shape information. This missing information is a function of the vision sensor viewpoint. The majority of the grasp synthesis algorithms in literature synthesize a grasp by using one single image of the target object and...

Active vision via extremum seeking for robots in unstructured environments: Applications in object recognition and manipulation

In this paper, a novel active vision strategy is proposed for optimizing the viewpoint of a robot's vision sensor for a given success criterion. The strategy is based on extremum seeking control (ESC), which introduces two main advantages: 1) Our approach is model free: It does not require an explicit objective function or any other task...

Fast grasping of unknown objects using principal component analysis

Fast grasping of unknown objects has crucial impact on the efficiency of robot manipulation especially subjected to unfamiliar environments. In order to accelerate grasping speed of unknown objects, principal component analysis is utilized to direct the grasping process. In particular, a single-view partial point cloud is constructed and...

Fast grasping of unknown objects using cylinder searching on a single point cloud

Grasping of unknown objects with neither appearance data nor object models given in advance is very important for robots that work in an unfamiliar environment. The goal of this paper is to quickly synthesize an executable grasp for one unknown object by using cylinder searching on a single point cloud. Specifically, a 3D camera is first used to...

Clutched elastic actuators

This paper identifies the class of actuators called clutched elastic actuators (CEAs). CEAs use clutches to control the energy flow into springs. CEAs in exoskeletons, prostheses, legged robots, and robotic arms have shown the ability to reduce the energy consumption and motor requirements, such as peak torque and peak power. Because of those...

Object grasping by combining caging and force closure

The current research trends of object grasping can be summarized as caging grasping and force closure grasping. The motivation of this paper is to combine the advantage of caging grasping and force closure grasping to enable under-actuated grippers like the Lacquey gripper and the parallel grippers like the PR2 gripper to quickly grasp the...

Unknown object grasping by using concavity

Reducing the grasp candidates for unknown object grasping while maintaining grasp stability is the goal of this paper. In this paper, we propose an efficient and straight forward unknown object grasping method by using concavities of the unknown objects to significantly reduce the grasp candidates. Shortest path concavity is first employed to...

Intrinsically Safe Robot Arm: Adjustable Static Balancing and Low Power Actuation

We present a design for a manipulator that is intrinsically mechanically safe, i.e. it can not cause pain (let alone damage) to a human being even if the control system has a failure. Based on the pressure pain thresholds for human skin, we derive a pinching safety constraint that limits the actuator torque, and an impact safety constraint that...

Fall detection in walking robots by multi-way principal component analysis

Large disturbances can cause a biped to fall. If an upcoming fall can be detected, damage can be minimized or the fall can be prevented. We introduce the multi-way principal component analysis (MPCA) method for the detection of upcoming falls. We study the detection capability of the MPCA method in a simulation study with the simplest walking...

Essentials of dynamic walking; analysis and design of two-legged robots

One of the main challenges in the design of human-like walking robots (useful for service or entertainment applications as well as the study of human locomotion) is to obtain dynamic locomotion, as opposed to the static form of locomotion demonstrated by most of the current prototypes. A promising concept is the idea of passive dynamic walking;...