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Understanding natural human activity involves not only identifying the action being performed, but also locating the semantic elements of the scene and describing the person's interaction with them. We present a system that is able to recognize complex, fine-grained human actions involving the manipulation of objects in realistic action sequences. Our method takes advantage of recent advances in sensors...
The studies on mirror neurons observed in monkeys indicate that recognition of other's actions activates neural circuits that are also responsible for generating the very same actions in the animal. The mirror neuron hypothesis argues that such an overlap between action generation and recognition can provide a shared worldview among individuals and be a key pillar for communication. Inspired by these...
Action classification is an important component of human-computer interaction. Trajectory classification is an effective way of performing action recognition with significant success reported in the literature. We compare two different representation schemes, raw multivariate time-series data and the covariance descriptors of the trajectories, and apply sparse representation techniques for classifying...
Inverse optimal control is the problem of computing a cost function that would have resulted in an observed sequence of decisions. The standard formulation of this problem assumes that decisions are optimal and tries to minimize the difference between what was observed and what would have been observed given a candidate cost function. We assume instead that decisions are only approximately optimal...
In this study, we propose a control method for movement assistive robots using measured signals from human users. Some of the wearable assistive robots have mechanisms that can be adjusted to human kinematics (e.g., adjustable link length). However, since the human body has a complicated joint structure, it is generally difficult to design an assistive robot which mechanically well fits human users...
In the near future, as robots become more advanced and affordable, we can envision their use as intelligent assistants in a variety of domains. An exemplar human-robot task identified in many previous works is cooperatively carrying a physically large object. An important task objective is to keep the carried object level. In this work, we propose an admittance-based controller that maintains a level...
The direct perception of actions allows a robot to predict the afforded actions of observed objects. In this paper, we present a non-parametric approach to representing the affordance-bearing subparts of objects. This representation forms the basis of a kernel function for computing the similarity between different subparts. Using this kernel function, together with motor primitive actions, the robot...
For robots of the future to interact seamlessly with humans, they must be able to reason about their surroundings and take actions that are appropriate to the situation. Such reasoning is only possible when the robot has knowledge of how the World functions, which must either be learned or hard-coded. In this paper, we propose an approach that exploits language as an important resource of high-level...
This paper presents a method for generating dynamically-feasible, natural-looking robot motion from freehand sketches. Using trajectory optimization, it handles sketches that are too fast, jerky, or pass out of reach by enforcing the constraints of the robot's dynamic limitations while minimizing the relative temporal differences between the robot's trajectory and the sketch. To make the optimization...
While human behavior prediction can increase the capability of a robotic partner to generate anticipatory behavior during physical human robot interaction (pHRI), predictions in uncertain situations can lead to large disturbances for the human if they do not match the human intentions. In this paper we present a novel control concept in which the assistive control parameters are adapted to the uncertainty...
Redundant tendon-driven systems such as the human hand or the ACT robotic hand are high-dimensional and nonlinear systems that make traditional control strategies ineffective. The synergy hypothesis from neuroscience suggests that employing dimensionality reduction techniques can simplify the system without a major loss in function. We define a dimensionality reduction framework consisting of separate...
Human behaviors are highly affected by implicit human factors such as culture, social conventions, laws and even the mental states of individuals and groups. If robots are to be accepted by humans, they must conform to common social norms and local customs and recognize highly socialized spatial behaviors. This paper aims to extend the Spatial Behavior Cognition Model (SBCM) proposed in [14] by adding...
Humans can perform fast and skillful manipulations using various parts of the body by effectively utilizing the dynamics of the targets. Visual sensation is the most important human sense used for such manipulations. Juggling is one such example involving skillful and dynamic manipulations, and visual information is essential for it to be successful. Previously, there have been several studies about...
Research in humanoid robotics in recent years has led to significant advances in terms of the ability to walk and even run. Yet, despite the general achievements in locomotion and control, energy efficiency is still one important area that requires further attention, especially as it is one of the major steeping stones leading to increased autonomy. This paper examines, and quantifies, the energetic...
Robots will more and more enter our daily life. In order to increase their acceptance it is necessary that their movements and behavior are predictable. With our present experiment we assess the acceptance of autonomous robots in human working and living environments. As a specific indicator we define legibility as an important prerequisite for user acceptance. In a simulator study participants rated...
Aimed at contextual mapping of environments by exploration, this paper proposes a method that recognises human activity observed from a moving camera and references this information to a previously mapped environment. We first introduce a novel method that uses sparse features and dense optical flow, to perform dense background subtraction for an agile camera. With the ego-motion disambiguated, we...
This paper proposes a new strategy for making a knot with a general-purpose arm. The strategy is divided into four stages: First, a human-performed knotting action is analyzed to acquire some motion primitives. Second, based on these databases, the robot is taught to tie a knot. Third, key points on the stored trajectories are extracted. These key points are considered in a general case, and then...
The paper concerns the control of a lower limb orthosis acting on the knee joint level. Therefore, a model of the shank-orthosis system is given considering the human effort as an external torque acting on the system. A model reference adaptive control law is developed and applied to the orthosis in order to make the system (shank-orthosis) track a desired trajectory predefined by a rehabilitation...
This paper proposes a method to construct Navigation Functions (NF) from experimental trajectories in an unknown environment. We want to approximate an unknown obstacle function and then use it within an NF. When navigating the same destinations with the experiments, this NF should produce the same trajectories as the experiments. This requirement is equivalent to a partial differential equation (PDE)...
Programming by Demonstration offers an intuitive framework for teaching robots how to perform various tasks without having to preprogram them. It also offers an intuitive way to provide corrections and refine teaching during task execution. Previously, mostly position constraints have been taken into account when teaching tasks from demonstrations. In this work, we tackle the problem of teaching tasks...
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