For a versatile human-assisting mobilemanipulating robot such as the PR2, handing over objects to humans in possibly cluttered workspaces is a key capability. In this paper we investigate the motion planning of handovers while accounting for the human mobility. We treat the human motion as part of the planning problem thus enabling to find broader type of handing strategies. We formalize the problem and propose an algorithmic solution taking into account the HRI constraints induced by the human receiver presence. Simulation results with the PR2 robot illustrate the efficacy of the approach.
We are combining symbolic and geometric planning to synthesize human-aware plans in order to deal with the complex and highly intricate planning problems induced by Human-Robot collaborative object manipulation.In this paper, we summarize our previous contributionsrefining symbolic actions at geometric level, during the symbolic planning, in order to assess their feasibility and computing the geometric side effects-, then we present the current contributions meant to tighten the cooperation between the symbolic planning and the geometric planning: the symbolic planner helps the geometric one by providing it with constraints and domain-expert knowledge making the geometric planner more efficient, and the geometric planner helps the symbolic one to find the best plan based on social costs computed at geometric level.We also propose different examples, highlighting the interest of such cooperation between the planners in simulation and on our PR2 robot.
Exchanging objects with humans through handovers is a key feature for any robot operating side by side with humans. The studies on the topic tackle various problems, such as the hand dexterity, the communication cues, the arms motions, the forces, the head motions, but most of them consider a handover as an independent action, decorrelated from the plan it is part of. In this paper, we consider situations where it might be necessary (or preferable) to achieve several handovers in order to transfer an object from one agent to another one. This problem complexity grows accordingly with the number of agents that might be involved in the task, making a classical approach under efficient. We propose a graph-based approach enabling a fast computation of a solution taking into account different parameters linked to the humans comfort and preferences. An abstract model of the task is also used as a heuristic to guide the search in the graph, a search which is performed with a Lazy Weighted A*. The method computes which agents (human or robot) to use and where handovers should be performed. It also computes motion plans for each robot, ensures that humans can reach handover places and preserves comfort of human partners by reducing, for instance, their efforts. The method has been implemented and is demonstrated on various simulated multi-agents environments and on our two PR2 robots interacting with two humans.
Handing-over objects to humans (or taking objects from them) is a key capability for a service robot. Humans are efficient and natural while performing this action and the purpose of the studies on this topic is to bring human-robot handovers to an acceptable, efficient and natural level. This paper deals with the cues that allow to make a handover look as natural as possible, and more precisely we focus on where the robot should look while performing it. In this context we propose a user study, involving 33 volunteers, who judged video sequences where they see either a human or a robot giving them an object. They were presented with different sequences where the agents (robot or human) have different gaze behaviours, and were asked to give their feeling about the sequence naturalness. In addition to this subjective measure, the volunteers were equipped with an eye tracker which enabled us to have more accurate objective measures.
This chapter deals with the integration of different planners that solve problems from different domains in a common context. In particular, a joint solution for structure assembly planning, symbolic task planning and geometric planning is presented and analyzed in the construction of structures with a team of aerial robots equipped with on-board manipulators in places where the access is difficult. Geometric reasoning is present at different levels in our joint solution in order to reduce the computational complexity derived from the highly dimensional space due to the many degrees of freedom of the robots and the complexity of the tasks and also to produce more robust plans even for strongly intricate problems.
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