Understanding of Object Manipulation Actions Using Human Multi-Modal Sensory Data

Object manipulation actions represent an important share of the Activities of Daily Living (ADLs). In this work, we study how to enable service robots to use human multi-modal data to understand object manipulation actions, and how they can recognize such actions when humans perform them during human-robot collaboration tasks. The multi-modal data in this study consists of videos, hand motion data, applied forces as represented by the pressure patterns on the hand, and measurements of the bending of the fingers, collected as human subjects performed manipulation actions. We investigate two different approaches. In the first one, we show that multi-modal signal (motion, finger bending and hand pressure) generated by the action can be decomposed into a set of primitives that can be seen as its building blocks. These primitives are used to define 24 multi-modal primitive features. The primitive features can in turn be used as an abstract representation of the multi-modal signal and employed for action recognition. In the latter approach, the visual features are extracted from the data using a pre-trained image classification deep convolutional neural network. The visual features are subsequently used to train the classifier. We also investigate whether adding data from other modalities produces a statistically significant improvement in the classifier performance. We show that both approaches produce a comparable performance. This implies that image-based methods can successfully recognize human actions during human-robot collaboration. On the other hand, in order to provide training data for the robot so it can learn how to perform object manipulation actions, multi-modal data provides a better alternative

Recognizing Intent in Collaborative Manipulation

Collaborative manipulation is inherently multimodal, with haptic communication playing a central role. When performed by humans, it involves back-and-forth force exchanges between the participants through which they resolve possible conflicts and determine their roles. Much of the existing work on collaborative human-robot manipulation assumes that the robot follows the human. But for a robot to match the performance of a human partner it needs to be able to take initiative and lead when appropriate. To achieve such human-like performance, the robot needs to have the ability to (1) determine the intent of the human, (2) clearly express its own intent, and (3) choose its actions so that the dyad reaches consensus. This work proposes a framework for recognizing human intent in collaborative manipulation tasks using force exchanges. Grounded in a dataset collected during a human study, we introduce a set of features that can be computed from the measured signals and report the results of a classifier trained on our collected human-human interaction data. Two metrics are used to evaluate the intent recognizer: overall accuracy and the ability to correctly identify transitions. The proposed recognizer shows robustness against the variations in the partner's actions and the confounding effects due to the variability in grasp forces and dynamic effects of walking. The results demonstrate that the proposed recognizer is well-suited for implementation in a physical interaction control scheme

Motion Planning in Humans and Robots

We present a general framework for generating trajectories and actuator forces that will take a robot system from an initial configuration to a goal configuration in the presence of obstacles observed with noisy sensors. The central idea is to find the motion plan that optimizes a performance criterion dictated by specific task requirements. The approach is motivated by studies of human voluntary manipulation tasks that suggest that human motions can be described as solutions of certain optimization problems

A Geometric Approach to the Study of the Cartesian Stiffness Matrix

The stiffness of a rigid body subject to conservative forces and moments is described by a tensor, whose components are best described by a 6×6 Cartesian stiffness matrix. We derive an expression that is independent of the parameterization of the motion of the rigid body using methods of differential geometry. The components of the tensor with respect to a basis of twists are given by evaluating the tensor on a pair of basis twists. We show that this tensor depends on the choice of an affine connection on the Lie group, SE(3). In addition, we show that the definition of the Cartesian stiffness matrix used in the literature [2,6] implicitly assumes an asymmetric connection and this results in an asymmetric stiffness matrix in a general loaded configuration. We prove that by choosing a symmetric connection we always obtain a symmetric Cartesian stiffness matrix. Finally, we derive stiffness matrices for different connections and illustrate the calculations using numerical examples

Robots Taking Initiative in Collaborative Object Manipulation: Lessons from Physical Human-Human Interaction

Physical Human-Human Interaction (pHHI) involves the use of multiple sensory modalities. Studies of communication through spoken utterances and gestures are well established. Nevertheless, communication through force signals is not well understood. In this paper, we focus on investigating the mechanisms employed by humans during the negotiation through force signals, which is an integral part of successful collaboration. Our objective is to use the insights to inform the design of controllers for robot assistants. Specifically, we want to enable robots to take the lead in collaboration. To achieve this goal, we conducted a study to observe how humans behave during collaborative manipulation tasks. During our preliminary data analysis, we discovered several new features that help us better understand how the interaction progresses. From these features, we identified distinct patterns in the data that indicate when a participant is expressing their intent. Our study provides valuable insight into how humans collaborate physically, which can help us design robots that behave more like humans in such scenarios

Continuous Methods for Motion Planning

Motion planning for a mechanical system addresses the problem of finding a trajectory and actuator forces that are consistent with a given set of constraints and perform a desired task. In general, the problem is under-determined and admits a large number of solutions. The main claim of this dissertation is that a natural way to resolve the indeterminacy is to define performance of a motion and find a solution with the best performance. The motion planning problem is thus formulated as a variational problem. The proposed approach is continuous in the sense that the motion planning problem is not discretized. A distinction is made between dynamic and kinematic motion planning. Dynamic motion planning provides the actuator forces as part of the motion plan and requires finding a motion that is consistent with the dynamic equations of the system, satisfies a given set of equality and inequality constraints, and minimizes a chosen cost functional. In kinematic motion planning, dynamic equations of the system are not taken into account and it is therefore simpler. For dynamic motion planning, a novel numerical method for solving a variational problem is developed. The method combines discretization of the continuous problem motivated by the finite-element analysis with techniques from nonlinear programming. It is used to find smooth trajectories and actuator forces for two planar cooperating manipulators holding an object. The method is then extended for systems that change the dynamic equations as they move. An example of a simple grasping task illustrates that for such systems variational approach unifies motion planning and task planning. Kinematic motion planning is formulated as a variational problem on a Riemannian manifold. A Riemannian metric and an affine connection are introduced to define cost functionals that measure smoothness of trajectories. Kinematic motion planning is normally used in the task space, which can be represented by the group of spatial rigid body displacements, SE(3). It is shown how the group structure of SE(3) can be used to find smooth trajectories that have certain invariance properties with respect to the choice of the inertial and body fixed frames

Review of the Literature on Time-Optimal Control of Robotic Manipulators

A task that robotic manipulators most frequently perform is motion between specified points in the working space. It is therefore important that these motions are efficient. The presence of the obstacles and other requirements of the task often require that the path is specified in advance. Robot actuators cannot generate unlimited forces/torques so it is reasonable to ask how to traverse the prespecified path in minimum time so that the limits on the actuator torques are not violated. It can be shown that the motion which requires least time to traverse a path requires at least one actuator to operate on the boundary (maximum or minimum). Furthermore, if the path is parameterized, the equations describing the robot dynamics can be rewritten as functions of the path parameter and its first and second derivatives. In general, the actuator bounds will be transformed into the bounds on the acceleration along the path. These bounds will be functions of the velocity and position. It is pos..

Review of the Literature on Time-Optimal Control of Robotic Manipulators

A task that robotic manipulators most frequently perform is motion between specified points in the working space. It is therefore important that these motions are efficient. The presence of the obstacles and other requirements of the task often require that the path is specified in advance. Robot actuators cannot generate unlimited forces/torques so it is reasonable to ask how to traverse the prespecified path in minimum time so that the limits on the actuator torques are not violated. It can be shown that the motion which requires least time to traverse a path requires at least one actuator to operate on the boundary (maximum or minimum). Furthermore, if the path is parameterized, the equations describing the robot dynamics can be rewritten as functions of the path parameter and its first and second derivatives. In general, the actuator bounds will be transformed into the bounds on the acceleration along the path. These bounds will be functions of the velocity and position. It is poss..