8,271 research outputs found
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
Survey on model-based manipulation planning of deformable objects
A systematic overview on the subject of model-based manipulation planning of deformable objects is presented. Existing modelling techniques of volumetric, planar and linear deformable objects are described, emphasizing the different types of deformation. Planning strategies are categorized according to the type of manipulation goal: path planning, folding/unfolding, topology modifications and assembly. Most current contributions fit naturally into these categories, and thus the presented algorithms constitute an adequate basis for future developments.Preprin
Contingent task and motion planning under uncertainty for human–robot interactions
Manipulation planning under incomplete information is a highly challenging task for mobile manipulators. Uncertainty can be resolved by robot perception modules or using human knowledge in the execution process. Human operators can also collaborate with robots for the execution of some difficult actions or as helpers in sharing the task knowledge. In this scope, a contingent-based task and motion planning is proposed taking into account robot uncertainty and human–robot interactions, resulting a tree-shaped set of geometrically feasible plans. Different sorts of geometric reasoning processes are embedded inside the planner to cope with task constraints like detecting occluding objects when a robot needs to grasp an object. The proposal has been evaluated with different challenging scenarios in simulation and a real environment.Postprint (published version
Planning Framework for Robotic Pizza Dough Stretching with a Rolling Pin
Stretching a pizza dough with a rolling pin is a nonprehensile manipulation. Since the object is deformable, force closure cannot be established, and the manipulation is carried out in a nonprehensile way. The framework of this pizza dough stretching application that is explained in this chapter consists of four sub-procedures: (i) recognition of the pizza dough on a plate, (ii) planning the necessary steps to shape the pizza dough to the desired form, (iii) path generation for a rolling pin to execute the output of the pizza dough planner, and (iv) inverse kinematics for the bi-manual robot to grasp and control the rolling pin properly. Using the deformable object model described in Chap. 3, each sub-procedure of the proposed framework is explained sequentially
Trajectory Optimization Through Contacts and Automatic Gait Discovery for Quadrupeds
In this work we present a trajectory Optimization framework for whole-body
motion planning through contacts. We demonstrate how the proposed approach can
be applied to automatically discover different gaits and dynamic motions on a
quadruped robot. In contrast to most previous methods, we do not pre-specify
contact switches, timings, points or gait patterns, but they are a direct
outcome of the optimization. Furthermore, we optimize over the entire dynamics
of the robot, which enables the optimizer to fully leverage the capabilities of
the robot. To illustrate the spectrum of achievable motions, here we show eight
different tasks, which would require very different control structures when
solved with state-of-the-art methods. Using our trajectory Optimization
approach, we are solving each task with a simple, high level cost function and
without any changes in the control structure. Furthermore, we fully integrated
our approach with the robot's control and estimation framework such that
optimization can be run online. By demonstrating a rough manipulation task with
multiple dynamic contact switches, we exemplarily show how optimized
trajectories and control inputs can be directly applied to hardware.Comment: Video: https://youtu.be/sILuqJBsyK
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