Showing papers by "Oussama Khatib published in 2016"

Ocean One: A Robotic Avatar for Oceanic Discovery

Abstract: The promise of oceanic discovery has long intrigued scientists and explorers, whether with the idea of studying underwater ecology and climate change or with the hope of uncovering natural resources and historic secrets buried deep in archaeological sites. This quest to explore the oceans requires skilled human access, yet much of the oceans are inaccessible to human divers; nearly ninetenths of the ocean floor is at 1 km or deeper [1]. Accessing these depths is imperative since factors such as pollution and deep-sea trawling threaten ecology and archaeological sites. While remotely operated vehicles (ROVs) are inadequate for the task, a robotic avatar could go where humans cannot and still embody human intelligence and intentions through immersive interfaces.

Robotics and the Handbook

Abstract: Robots! Robots on Mars and in oceans, in hospitals and homes, in factories and schools; robots fighting fires, making goods and products, saving time and lives. Robots today are making a considerable impact on many aspects of modern life, from industrial manufacturing to healthcare, transportation, and exploration of the deep space and sea. Tomorrow, robots will be as pervasive and personal as today’s personal computers. This chapter retraces the evolution of this fascinating field from the ancient to the modern times through a number of milestones: from the first automated mechanical artifact (1400 BC) through the establishment of the robot concept in the 1920s, the realization of the first industrial robots in the 1960s, the definition of robotics science and the birth of an active research community in the 1980s, and the expansion towards the challenges of the human world of the twenty-first century. Robotics in its long journey has inspired this handbook which is organized in three layers: the foundations of robotics science; the consolidated methodologies and technologies of robot design, sensing and perception, manipulation and interfaces, mobile and distributed robotics; the advanced applications of field and service robotics, as well as of human-centered and life-like robotics.

Adaptive human-inspired compliant contact primitives to perform surface-surface contact under uncertainty

Abstract: This paper focuses on devising a control policy that is inspired by human strategy to enable robots to perform surface-surface contact between a hand-held object e.g. a box and the environment e.g. table. We assume the object's shape is partially known and consider uncertainties in both the environment and the object grasp. Our analysis on ten untrained subjects indicates that during this task: 1 the subjects start decreasing the angular velocity before the complete alignment in contrast to existing approaches, and 2 they do not control the contact force to remain at a fixed value. Our study is also consistent with the hypothesis that the subjects determine an over-estimate of the relative angle between the object in hand and the environment. Based on these observations we propose a novel control policy, called Surface-Surface Contact Primitive SSCP, which can perform the surface-surface alignment task with only partial information about the hand-held object and the environment. Furthermore, SSCP only requires a rough estimate of the surface normal vector and does not rely on either the estimation of contact type i.e. point or edge contact or locations of contact points. We evaluate the performance of the proposed controller on a set of robot experiments using two seven-degree-of-freedom robots, one for imposing uncertainty to the environment, and the other to perform the experiment. We show the applicability of the controller on four objects with different geometries, its generalization to four different surface materials, and its robustness to uncertainty in environment and grasp as well as external perturbations.

From Bot to Bot: Using a Chat Bot to Synthesize Robot Motion.

Abstract: We present Bot to Bot, a system for developers to write voice controlled applications in a high-level language while retaining portability over a variety of different robot hardware platforms. In this paper we describe how Bot to Bot leverages advances in natural language processing and robotic control to take a user’s voice command and translate it into a structured intent for the robot through the following intermediate representations: verbal bites, robot assembly, and robot control primitives. Our long-term goal is to find a verbal instruction set for human-robot interaction. We provide our software as open source to encourage future research.

Using Haptic fMRI to Enable Interactive Motor Neuroimaging Experiments

Abstract: Combining haptics with functional magnetic resonance imaging (Haptic fMRI) has enabled complex motor neuroimaging experiments that non-invasively map real-world motor tasks on to the human brain. The technique’s resolution, fidelity and susceptibility to scanning artifacts, however, have not yet been estimated in a quantitative manner. Here, we demonstrate that unconstrained three degree-of-freedom Haptic fMRI experiments can reliably activate brain regions involved in planning, motor control, haptic perception, and vision. We show that associated neural measurements are reliable, heterogeneous at the millimeter scale, and free from measurable artifacts, and that their anatomical localization is consistent with past neuroscience experiments. In addition, we demonstrate the feasibility of using electromagnetic actuation in Haptic fMRI interfaces to apply high fidelity open-loop three-axis haptic forces (0.5–2N; square or 0.1–65Hz sine waveforms) while maintaining negligible temporal noise in pre-motor, motor, somatosensory, and visual cortex (<1 % of signal). Our results show that Haptic fMRI is a robust and reliable technique for characterizing the human brain’s motor controller.

A Framework for Real-Time Multi-Contact Multi-Body Dynamic Simulation

Abstract: In this paper we propose a unified framework for the real-time dynamic simulation and contact resolution of rigid articulated bodies. This work builds on previous developments in the field of dynamic simulation, collision detection, contact resolution, and operational space control. However, the key to efficiency and real-time performance is a new parallel implementation of our collision detection and contact resolution algorithm which decomposes the problem into tasks that can be concurrently executed. Finally, the results and accuracy of our simulation models are compared for the first time against recorded motions of real articulated bodies colliding on a frictionless air floating table.

Experimental Analysis of Human Control Strategies in Contact Manipulation Tasks

Abstract: Here, we present insights into human contact-control strategies by defining conditions to determine whether a human controls a contact state, empirically analyzing object-to-environment contact geometry data obtained from human demonstrations in a haptic simulation environment, and testing hypothesess about underlying human contact-control strategies. Using haptic demonstration data from eleven subjects who inserted non-convex objects into occluded holes, we tested the following human contact-control hypotheses: (h1) humans follow a task trajectory that tracks pre-planned contact-state waypoints organized in a contact-state graph (contact-waypoint hypothesis); (h2) humans traverse the contact-state graph, explicitly controlling some contact states or subsets of contact states, in addition to the pre-determined initial and final goal states (controlled subgraph hypothesis); (h3) humans use a control policy where the only controlled states are the starting state for the task and the goal state (state policy hypothesis). Notably, we found that humans tend to visit a select few contact states once they enter each state’s vicinity in the graph, which is evidence against h3. Yet humans do not always visit said states (visit probability \(<40\%\)), which is, in addition, evidence against h1 provided different humans adopt similar strategies. We show that a classifier to determine when humans control their trajectories to visit specific contact states, when parameterized correctly, is invariant to graph aggregation operations across the false-positive to false-negative tradeoff spectrum. This indicates our results are robust given the data we obtained and suggests that efforts to characterize human motion should focus on h2.

Muscular Effort for the Characterization of Human Postural Behaviors

Abstract: The human selection of specific postures among the infinity of possibilities is the result of a long and complex process of learning. Through learning, humans seem to come to discover the properties of their bodies and how best to put them to use when performing a task. Exploiting the body’s kinematic characteristics, humans effectively use the body’s mechanical advantage to improve the transmission of the muscles’ tension into the forces the task requires. However, the efficiency of this transmission is also affected by the human muscle actuation physiology and dynamics. By also adjusting the body configurations to maximize this transmission of muscle tensions to resulting task forces, humans are in fact exploiting what can be termed the physiomechanical advantage of their musculoskeletal system. Here, we investigate the physiomechanical advantage of humans through several experimental validations. Based on the results of the analysis, we conclude that in learned tasks the optimization of the physiomechanical advantage corresponds to the overall minimization of the human muscular effort. The approach presented here can be applied for the motion control of human musculoskeletal models where the control is task-driven and the task consistent postures are driven by the muscular criteria.

A Centrifugal Force-Based Configuration-Independent High-Torque-Density Passive Brake for Human-Friendly Robots

Abstract: Safe actuation is one of the most important requirements for human–robot collaboration. Although a variety of passive brakes have been developed in order to safely regulate joint velocities, their performances are significantly subjective to gravity direction and mounting position, and thus are not suitable for multi-degrees of freedom (DoF) robotic applications. Addressing these issues, we developed a centrifugal force-based configuration-independent high-torque-density passive brake. The brake is rapidly and bidirectionally activated at the desired velocity limit in any orientation relative to the direction of gravity. A design optimization methodology is proposed for high-torque density and low-reflected inertia, which allows for inherent safe actuation in the event of a system failure. Experimental results demonstrate that the proposed brake is an effective solution for limiting velocity in multi-DoF human-friendly robots.

A parameterized family of anatomically accurate human upper-body musculoskeletal models for dynamic simulation & control

Abstract: Modeling human motion requires an accurate specification of musculoskeletal physiology, yet there exists no method to quantify the modeling accuracy required, or to predict the effect of modeling errors on subsequent analyses. Quantifying how inaccuracies in physiology, kinematics, or dynamics affect the study of human motion is a challenge that must be solved before we can construct robust generative models of human motor control at appropriate levels of detail. In this paper, we overcome two fundamental problems in characterizing the effect of model accuracy: the lack of ground truth about the arm's musculoskeletal kinematics, and the inability to systematically vary modeling accuracy. To do so, we developed a family of upper-body musculoskeletal models for a live human individual where modeled musculature was parameterized by decomposing volumetric muscles into fiber-groups of varying diameter and geometric complexity. The family of models thus obtained offer an unprecedented level of detail, and enable empirical comparisons of human motion analysis results across varying levels of anatomical accuracy and geometry. This sets the stage for large-scale studies of human motion that connect high level behavior to low level musculoskeletal dynamics, with applications in robotics, biomechanics, and human motor control.

The New Robotics Age: Meeting the Physical Interactivity Challenge

Abstract: The generations of robots now being developed will increasingly touch people and their lives. They will explore, work, and interact with humans in their homes, workplaces, in new production systems, and in challenging field domains. The emerging robots will provide increased operational support in mining, underwater, and in hostile and dangerous environments. While full autonomy for the performance of advanced tasks in complex environments remains challenging, strategic intervention of a human will tremendously facilitate reliable real-time robot operations. Human-robot synergy benefits from combining the experience and cognitive abilities of the human with the strength, dependability, competence, reach, and endurance of robots. Moving beyond conventional teleoperation, the new paradigm—placing the human at the highest level of task abstraction—relies on robots with the requisite physical skills for advanced task behavior capabilities. Such connecting of humans to increasingly competent robots will fuel a wide range of new robotic applications in places where they have never gone before. This discussion focuses on robot design concepts, robot control architectures, and advanced task primitives and control strategies that bring human modeling and skill understanding to the development of safe, easy-to-use, and competent robotic systems. The presentation will highlight these developments in the context of a novel underwater robot, Ocean One, called O2, developed at Stanford in collaboration with Meka Robotics, and KAUST.

Workpiece contact state estimating device and workpiece contact state estimation method

Abstract: The present invention provides a workpiece contact state estimating device and a workpiece contact state estimation method to estimate an actual state of contact of a workpiece A with a peripheral object on the basis of a feasible contact acting force range, which is a feasible range of the value of a contact acting force (the force acting on a manipulator 1 generated by contact) prepared in advance for each of a plurality of types of contact states that are feasible as the states of contact of the workpiece A with the peripheral object, and a measurement value of the contact acting force at the time of contact of the workpiece A with the peripheral object.

Guest Editorial of the Special Issue on Whole-Body Control for Robots in the Real World

Abstract: Research in whole-body control (WBC) aims to contribute to provide robots with those capabilities that are necessary to move and perform in real world scenarios. Until recent years, limitations on hardware relegated WBC to almost purely theoretical research. Recently, a growing number of experimental platforms have become available (in particular, torque-controlled humanoids). This new opportunity has triggered the deployment on real robots of the theoretical outcomes of research in the field. This is backed up by a number of new research projects and initiatives addressing issues in this domain, including the Darpa robotic challenge (DRC). The goal of this special issue is to provide a clear representation of what is the state-of-the-art in WBC, and to help identifying what steps still need to be taken to have humanoid robots moving out of research laboratories to real world applications.