Figure 1: Interactive computational design of quadrotor trajectories: (A) user interface to specifiy keyframes and dynamics of quadrotor flight. (B) An optimization algorithm generates feasible trajectories and (C) a 3D preview allows the user to quickly iterate on them. (D) The final motion plan can be flown by real quadrotors. The tool enables the implementation of a number of compelling use cases such as (B) robotic light-painting, aerial racing and (D) aerial videography. ABSTRACTIn this paper we propose a computational design tool that allows end-users to create advanced quadrotor trajectories with a variety of application scenarios in mind. Our algorithm allows novice users to create quadrotor based use-cases without requiring deep knowledge in either quadrotor control or the underlying constraints of the target domain. To achieve this goal we propose an optimization-based method that generates feasible trajectories which can be flown in the real world. Furthermore, the method incorporates high-level human objectives into the planning of flight trajectories. An easy to use 3D design tool allows for quick specification and editing of trajectories as well as for intuitive exploration of the resulting solution space. We demonstrate the utility of our approach in several real-world application scenarios, including aerial-videography, robotic light-painting and drone racing.
In this paper, we propose a method for training control policies for human-robot interactions such as handshakes or hand claps via Deep Reinforcement Learning. The policy controls a humanoid Shadow Dexterous Hand, attached to a robot arm. We propose a parameterizable multi-objective reward function that allows learning of a variety of interactions without changing the reward structure. The parameters of the reward function are estimated directly from motion capture data of human-human interactions in order to produce policies that are perceived as being natural and human-like by observers. We evaluate our method on three significantly different hand interactions: handshake, hand clap and finger touch. We provide detailed analysis of the proposed reward function and the resulting policies and conduct a large-scale user study, indicating that our policy produces natural looking motions.
Fig. 1. Quadrotor camera tools generate trajectories based on user-specified keyframes in time and space. Reasoning about spatio-temporal distances is hard for users and can lead to visually unappealing results and fluctuating camera velocities. Top row: user-specified keyframes (blue) are positioned in time, such that the camera first moves too slow and then needs to accelerate drastically to reach the specified end-point. Bottom row: results of our method which automatically positions keyframes (blue) in time such that the camera moves smoothly over the entire trajectory (illustrative example).In this paper we first contribute a large scale online study (N ≈ 400) to better understand aesthetic perception of aerial video. The results indicate that it is paramount to optimize smoothness of trajectories across all keyframes. However, for experts timing control remains an essential tool. Satisfying this dual goal is technically challenging because it requires giving up desirable properties in the optimization formulation. Second, informed by this study we propose a method that optimizes positional and temporal reference fit jointly. This allows to generate globally smooth trajectories, while retaining user control over reference timings. The formulation is posed as a variable, infinite horizon, contour-following algorithm. Finally, a comparative lab study indicates that our optimization scheme outperforms the state-of-theart in terms of perceived usability and preference of resulting videos. For novices our method produces smoother and better looking results and also experts benefit from generated timings.
We propose an approach to capture subjective first-person view (FPV) videos by drones for automated cinematography. FPV shots are intentionally not smooth to increase the level of immersion for the audience, and are usually captured by a walking camera operator holding traditional camera equipment. Our goal is to automatically control a drone in such a way that it imitates the motion dynamics of a walking camera operator, and, in turn, capture FPV videos. For this, given a user-defined camera path, orientation, and velocity, we first present a method to automatically generate the operator’s motion pattern and the associated motion of the camera, considering the damping mechanism of the camera equipment. Second, we propose a general computational approach that generates the drone commands to imitate the desired motion pattern. We express this task as a constrained optimization problem, where we aim to fulfill high-level user-defined goals, while imitating the dynamics of the walking camera operator and taking the drone’s physical constraints into account. Our approach is fully automatic, runs in real time, and is interactive, which provides artistic freedom in designing shots. It does not require a motion capture system, and works both indoors and outdoors. The validity of our approach has been confirmed via quantitative and qualitative evaluations.
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