Probabilistic movement primitives for coordination of multiple human–robot collaborative tasks

This paper proposes an interaction learning method for collaborative and assistive robots based on movement primitives. The method allows for both action recognition and human–robot movement coordination. It uses imitation learning to construct a mixture model of human–robot interaction primitives. This probabilistic model allows the assistive trajectory of the robot to be inferred from human observations. The method is scalable in relation to the number of tasks and can learn nonlinear correlations between the trajectories that describe the human–robot interaction. We evaluated the method experimentally with a lightweight robot arm in a variety of assistive scenarios, including the coordinated handover of a bottle to a human, and the collaborative assembly of a toolbox. Potential applications of the method are personal caregiver robots, control of intelligent prosthetic devices, and robot coworkers in factories

Manipulation planning under changing external forces

This paper presents a planner that enables robots to manipulate objects under changing external forces. Particularly, we focus on the scenario where a human applies a sequence of forceful operations, e.g. cutting and drilling, on an object that is held by a robot. The planner produces an efficient manipulation plan by choosing stable grasps on the object, by intelligently deciding when the robot should change its grasp on the object as the external forces change, and by choosing subsequent grasps such that they minimize the number of regrasps required in the long-term. Furthermore, as it switches from one grasp to the other, the planner solves the bimanual regrasping in the air by using an alternating sequence of bimanual and unimanual grasps. We also present a conic formulation to address force uncertainties inherent in human-applied external forces, using which the planner can robustly assess the stability of a grasp configuration without sacrificing planning efficiency. We provide a planner implementation on a dual-arm robot and present a variety of simulated and real human-robot experiments to show the performance of our planner

A closed-loop approach for tracking a humanoid robot using particle filtering and depth data

Humanoid robots introduce instabilities during biped march that complicate the process of estimating their position and orientation along time. Tracking humanoid robots may be useful not only in typical applications such as navigation, but in tasks that require benchmarking the multiple processes that involve registering measures about the performance of the humanoid during walking. Small robots represent an additional challenge due to their size and mechanic limitations which may generate unstable swinging while walking. This paper presents a strategy for the active localization of a humanoid robot in environments that are monitored by external devices. The problem is faced using a particle filter method over depth images captured by an RGB-D sensor in order to effectively track the position and orientation of the robot during its march. The tracking stage is coupled with a locomotion system controlling the stepping of the robot toward a given oriented target. We present an integral communication framework between the tracking and the locomotion control of the robot based on the robot operating system, which is capable of achieving real-time locomotion tasks using a NAO humanoid robot.The final publication is available at Springer via http://dx.doi.org/10.1007/s11370-017-0230-0Peer Reviewe

Legibility of robot approach trajectories with minimum jerk path planning

When a robot approaches a person, the chosen trajectory ideally informs the person not only about the robot’s intended target location, but also its intended orientation. However, planning a straight line to the goal location does not guarantee a correct final orientation, potentially causing confusion as the robot eventually rotates towards its unsuspecting target. One method that could remedy this problem is minimum jerk path planning, which results in the smoothest possible path that ends in the pre-specified final orientation. The technique is already widely used in robotic arm motion planning, but existing work is lacking for regular path planning. The aim of the current study is to implement minimum jerk path planning for the Nao robot and to evaluate the potential benefit for human observers to infer the intended target of the robot. Results show that minimum jerk path planning significantly improves people’s recognition of the robot’s destination compared to straight line path planning. Meanwhile, the perceived likeability and human likeness of the robot remain the same, suggesting that implementing smooth robot path planning that includes the final orientation leads to more predictable robot approaching behaviour.KeywordsHuman-aware navigation Path planning Robot intention Human-robot interactio

Comfortable passing distances for robots

If autonomous robots are expected to operate in close proximity with people, they should be able to deal with human proxemics and social rules. Earlier research has shown that robots should respect personal space when approaching people, although the quantitative details vary with robot model and direction of approach. It would seem that similar considerations apply when a robot is only passing by, but direct measurement of the comfort of the passing distance is still missing. Therefore the current study measured the perceived comfort of varying passing distances of the robot on each side of a person in a corridor. It was expected that comfort would increase with distance until an optimum was reached, and that people would prefer a left passage over a right passage. Results showed that the level of comfort did increase with distance up to about 80 cm, but after that it remained constant. There was no optimal distance. Surprisingly, the side of passage had no effect on perceived comfort. These findings show that robot proxemics for passing by differ from approaching a person. The implications for modelling human-aware navigation and personal space models are discussed

Synthesizing Robot Motions Adapted to Human Presence

International audienceWith robotics hardware becoming more and more safe and compliant, robots are not far from entering our homes. The robot, that will share the same environment with humans, will be expected to consider the geometry of the interaction and to perform intelligent space sharing.In this case, even the simplest tasks, e.g. handing over an object to a person, raise important questions such as: where the task should be achieved?; how to place the robot relatively to the human in order to ease the human action?; how to hand over an object?; and more generally, how to move in a relatively constrained environment in the presence of humans?In this paper we present an integrated motion synthesis framework from planning to execution that is especially designed for a robot that interacts with humans. This framework, composed of Perspective Placement, Human Aware Manipulation Planner and Soft Motion Trajectory Planner, generates robot motions by taking into account human’s safety; his vision field and his perspective; his kinematics and his posture along with the task constraints