Legged robots pose one of the greatest challenges in robotics. Dynamic and agile maneuvers of animals cannot be imitated by existing methods that are crafted by humans. A compelling alternative is reinforcement learning, which requires minimal craftsmanship and promotes the natural evolution of a control policy. However, so far, reinforcement learning research for legged robots is mainly limited to simulation, and only few and comparably simple examples have been deployed on real systems. The primary reason is that training with real robots, particularly with dynamically balancing systems, is complicated and expensive. In the present work, we introduce a method for training a neural network policy in simulation and transferring it to a state-of-the-art legged system, thereby leveraging fast, automated, and cost-effective data generation schemes. The approach is applied to the ANYmal robot, a sophisticated medium-dog-sized quadrupedal system. Using policies trained in simulation, the quadrupedal machine achieves locomotion skills that go beyond what had been achieved with prior methods: ANYmal is capable of precisely and energy-efficiently following high-level body velocity commands, running faster than before, and recovering from falling even in complex configurations.

/pdf/learning-agile-and-dynamic-motor-skills-for-legged-robots-41hdxn2m1m.pdf

Learning agile and dynamic motor skills for legged robots

We present a new collision and proximity library that integrates several techniques for fast and accurate collision checking and proximity computation. Our library is based on hierarchical representations and designed to perform multiple proximity queries on different model representations. The set of queries includes discrete collision detection, continuous collision detection, separation distance computation and penetration depth estimation. The input models may correspond to triangulated rigid or deformable models and articulated models. Moreover, FCL can perform probabilistic collision checking between noisy point clouds that are captured using cameras or LIDAR sensors. The main benefit of FCL lies in the fact that it provides a unified interface that can be used by various applications. Furthermore, its flexible architecture makes it easier to implement new algorithms within this framework. The runtime performance of the library is comparable to state of the art collision and proximity algorithms. We demonstrate its performance on synthetic datasets as well as motion planning and grasping computations performed using a two-armed mobile manipulation robot.

/pdf/fcl-a-general-purpose-library-for-collision-and-proximity-38x7mn66pn.pdf

FCL: A general purpose library for collision and proximity queries

A compilation of UAV applications for precision agriculture

Due to occlusions, the estimation of the full pose of a human hand interacting with an object is much more challenging than pose recovery of a hand observed in isolation. In this work we formulate an optimization problem whose solution is the 26-DOF hand pose together with the pose and model parameters of the manipulated object. Optimization seeks for the joint hand-object model that (a) best explains the incompleteness of observations resulting from occlusions due to hand-object interaction and (b) is physically plausible in the sense that the hand does not share the same physical space with the object. The proposed method is the first that solves efficiently the continuous, full-DOF, joint hand-object tracking problem based solely on markerless multicamera input. Additionally, it is the first to demonstrate how hand-object interaction can be exploited as a context that facilitates hand pose estimation, instead of being considered as a complicating factor. Extensive quantitative and qualitative experiments with simulated and real world image sequences as well as a comparative evaluation with a state-of-the-art method for pose estimation of isolated hands, support the above findings.

Full DOF tracking of a hand interacting with an object by modeling occlusions and physical constraints

This report presents a survey of computer based simulators for unmanned vehicles. The simulators examined cover a wide spectrum of vehicles including unmanned aerial vehicles, both full scale and micro size; unmanned surface and subsurface vehicles; and unmanned ground vehicles. The majority of simulators use simple numerical simulation and simplistic visualization using custom OpenGL code. An emerging trend is to used modified commercial game engines for physical simulation and visualization. The game engines that are commercially available today are capable of physical simulations providing basic physical properties and interactions between objects. Newer and/or specialized engines such as the flight simulator X-Plane or Ageia PhysX and Havok physics engines, are capable of simulating more complex physical interactions between objects. Researchers in need of a simulator have a choice of using game engines or available open source and commercially available simulators, allowing resources to be focused on research instead of building a new simulator. We conclude that it is no longer necessary to build a new simulator from scratch.

/pdf/a-survey-of-commercial-open-source-unmanned-vehicle-129isq6n6u.pdf

祐山田

Papers

Open Dynamics Engine を用いたスノーボードロボットシミュレータの開発

祐 山田

Papers

Open Dynamics Engine を用いたスノーボードロボットシミュレータの開発

祐山田