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As robots find applications in daily life conditions it becomes important to develop controllers that generate energy efficient movements by restricting variability and utilizing high gains only when necessary. Here we present a computationally light and energy efficient approach (AAC) that combines an anticipatory open-loop controller and a variable gain closed loop controller. The approach is grounded...
The ability to recognize human actions is a fundamental problem in many areas of robotics research concerned with human-robot interaction or learning from human demonstration. In this paper, we present a new integrated approach to identifying and recognizing actions in human movement sequences and their reproduction in unknown situations. We propose a set of task-space features to construct probabilistic...
In this paper, we propose a control scheme that allows a humanoid robot to perform a transportation task jointly with a human partner. From the study of how human dyads achieve such a task, we have developed a control law for physical interaction that unifies standalone and collaborative (leader and follower) modes for trajectory-based tasks. We present it in the case of a linear impedance controller...
Proactive physical robotic assistance in the presence of human prediction uncertainty is a very challenging control problem. In this paper we propose a risk-sensitive optimal feedback controller for physical assistance that autonomously adapts the robot's behavior even during unknown situations. Using a probabilistic model to represent the cooperative task execution behavior and modeling the human...
In this paper we describe an event-based walking control system for biped robots. Classically, the control of biped robots has been separated into walking pattern generation and stabilizing control. While this is an effective strategy in well-known environments, walking in rough, unmodelled terrain can easily destabilize the system. Findings from neurobiology suggest that gait generation in animals...
Throwing is a complex and highly dynamic task. Humans usually exploit passive dynamics of their limbs to optimize their movement and muscle activation. In order to approach human throwing, we developed a double pendulum robotic platform. To introduce passivity into the actuated joints, clutches were included in the drive train. In this paper, we demonstrate the advantage of exploiting passive dynamics...
In this research, an approach to optimize motions for a humanoids is presented. Rapidly-exploring Random Tree(RRT) were used to plan an initial suboptimal motion. A reinforcement learning was then implemented to optimize the trajectories with respect to energy consumption, similarity to a human's natural motion and, physical limits. Energy cost was estimated by joint torque from a dynamic model, and...
Goal-directed physical assistance to the human is one of the most challenging problems in the area of human-robot interaction. Planning and learning from demonstration represent two conceptually different approaches to achieve goal-directed behavior. Here we examine the properties of a planning-based and a learning-based approach in the context of physical robotic assistance for the prototypical task...
Flexible needle with bevel tip offers greater mobility for puncture surgery. This would expand the scope of the puncture surgery. However, motion planning for flexible needle is still a challenge due to its non-holonomic property and the complicated interactions with soft tissues. In this paper, a multilayer tissue model is constructed to simulate human tissue, and a dynamic programming is employed...
In this paper, the use of a coordinate-free representation for recognizing six DOF rigid body motion trajectories is experimentally validated. In the recognition part of this approach, the three-dimensional measured position trajectories of arbitrary and uncalibrated points attached to the rigid body are transformed to an invariant, coordinate-free representation of the rigid body motion trajectory...
Human arm trajectory formations have been widely investigated to understand how we control the trajectories in the central nervous system. The trajectory formations proposed in most of studies are for now limited to a plane motion, and the arm model used for analyses are mostly restricted within three degrees of freedom. On the contrary, the structure of the human arm has redundancy for locating a...
This paper proposes a human movement trajectory recording method by a thermopile array sensor. In the system, the sensor is attached to the ceiling and it acquires place-dependent temperatures, which is called thermal distribution. The system obtains 4 × 4 pixels thermal distributions from the sensor. The distributions are analyzed to extract human movement trajectory. First, human candidate pixels...
This paper deals with the problem of computing trajectories for an exoskeleton that match a motion recorded on a given subject. Literature suggests that this problem can be solved by reconstructing the subject's joint motion using one of the numerous models available, and then feeding the exoskeleton with the joint trajectories. This is founded on the assumption that the exoskeleton kinematics reproduces...
Reaching is a fundamental skill for a robot. The purpose of robot reaching is to bring a robot hand to an object without any obstacle collision. Conventional handcrafted methods were complicated to implement reaching skill. Thus, this paper proposes a method using primitives acquired from human demonstrations to learn collision-free reaching skill. End-effector and joint trajectories of primitives...
Category 4. A machine learning based methodology is proposed to recognize a predefined set of hand gestures using depth images. For such purpose, a RGBD sensor (Microsoft kinect) is employed to track the hand position. Thus, a preprocessing stage is presented to subtract the region of interest from depth images. Moreover, a learning algorithm based on kernel methods is used to discover the relationships...
In the field of video surveillance, multiple object tracking is a challenging problem in the real application. In this paper, we propose a multiple object tracking method by spatiotemporal tracklet association. Firstly, reliable tracklets, the fragments of the entire trajectory of individual object movement, are generated by frame-wise association between object localization results in the neighbor...
This work compares the learning of linear evaluation functions using preference learning versus least squares temporal difference learning, LSTD(λ), from samples of game trajectories. The game trajectories are taken from human competitions held by the French Othello Federation1. The raw board positions are used to create a linear evaluation function to illustrate the key difference between the two...
An innovative automatic door control system is proposed in this paper to overcome the drawback of frequent false actions among the present devices while increasing their added values for security applications. Through human detection and trajectory tracking techniques, the proposed system can precisely identify those people with intention of entering or leaving the door, and then control opening and...
Driving includes many social factors, such as interaction with other vehicles. In this study, we attempted to examine the social interactions in driving through developing a novel driver assistance system; this system is intended to aid non-expert drivers in assessing such interactions. We first present a novel networked driving simulator based on SIGVerse which is a software platform for research...
The psychological overcharge issue related to human inadequacy to maintain a constant level of attention in simultaneously monitoring multiple visual information sources makes necessary to develop enhanced video surveillance systems that automatically understand human behaviors and identify dangerous situations. This paper introduces a semantic human behavioral analysis (HBA) system based on a neuro-fuzzy...
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