1,119 research outputs found
Supervised Autonomous Locomotion and Manipulation for Disaster Response with a Centaur-like Robot
Mobile manipulation tasks are one of the key challenges in the field of
search and rescue (SAR) robotics requiring robots with flexible locomotion and
manipulation abilities. Since the tasks are mostly unknown in advance, the
robot has to adapt to a wide variety of terrains and workspaces during a
mission. The centaur-like robot Centauro has a hybrid legged-wheeled base and
an anthropomorphic upper body to carry out complex tasks in environments too
dangerous for humans. Due to its high number of degrees of freedom, controlling
the robot with direct teleoperation approaches is challenging and exhausting.
Supervised autonomy approaches are promising to increase quality and speed of
control while keeping the flexibility to solve unknown tasks. We developed a
set of operator assistance functionalities with different levels of autonomy to
control the robot for challenging locomotion and manipulation tasks. The
integrated system was evaluated in disaster response scenarios and showed
promising performance.Comment: In Proceedings of IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS), Madrid, Spain, October 201
Tapered whisker reservoir computing for real-time terrain identification-based navigation
This paper proposes a new method for real-time terrain recognition-based navigation for mobile robots. Mobile robots performing tasks in unstructured environments need to adapt their trajectories in real-time to achieve safe and efficient navigation in complex terrains. However, current methods largely depend on visual and IMU (inertial measurement units) that demand high computational resources for real-time applications. In this paper, a real-time terrain identification-based navigation method is proposed using an on-board tapered whisker-based reservoir computing system. The nonlinear dynamic response of the tapered whisker was investigated in various analytical and Finite Element Analysis frameworks to demonstrate its reservoir computing capabilities. Numerical simulations and experiments were cross-checked with each other to verify that whisker sensors can separate different frequency signals directly in the time domain and demonstrate the computational superiority of the proposed system, and that different whisker axis locations and motion velocities provide variable dynamical response information. Terrain surface-following experiments demonstrated that our system could accurately identify changes in the terrain in real-time and adjust its trajectory to stay on specific terrain
Extreme Parkour with Legged Robots
Humans can perform parkour by traversing obstacles in a highly dynamic
fashion requiring precise eye-muscle coordination and movement. Getting robots
to do the same task requires overcoming similar challenges. Classically, this
is done by independently engineering perception, actuation, and control systems
to very low tolerances. This restricts them to tightly controlled settings such
as a predetermined obstacle course in labs. In contrast, humans are able to
learn parkour through practice without significantly changing their underlying
biology. In this paper, we take a similar approach to developing robot parkour
on a small low-cost robot with imprecise actuation and a single front-facing
depth camera for perception which is low-frequency, jittery, and prone to
artifacts. We show how a single neural net policy operating directly from a
camera image, trained in simulation with large-scale RL, can overcome imprecise
sensing and actuation to output highly precise control behavior end-to-end. We
show our robot can perform a high jump on obstacles 2x its height, long jump
across gaps 2x its length, do a handstand and run across tilted ramps, and
generalize to novel obstacle courses with different physical properties.
Parkour videos at https://extreme-parkour.github.io/Comment: Website and videos at https://extreme-parkour.github.io
Mobile Robotics
The book is a collection of ten scholarly articles and reports of experiences and perceptions concerning pedagogical practices with mobile robotics.“This work is funded by CIEd – Research Centre on Education, project UID/CED/01661/2019, Institute of Education, University of Minho, through national funds of FCT/MCTES-PT.
Efficient Humanoid Contact Planning using Learned Centroidal Dynamics Prediction
Humanoid robots dynamically navigate an environment by interacting with it
via contact wrenches exerted at intermittent contact poses. Therefore, it is
important to consider dynamics when planning a contact sequence. Traditional
contact planning approaches assume a quasi-static balance criterion to reduce
the computational challenges of selecting a contact sequence over a rough
terrain. This however limits the applicability of the approach when dynamic
motions are required, such as when walking down a steep slope or crossing a
wide gap. Recent methods overcome this limitation with the help of efficient
mixed integer convex programming solvers capable of synthesizing dynamic
contact sequences. Nevertheless, its exponential-time complexity limits its
applicability to short time horizon contact sequences within small
environments. In this paper, we go beyond current approaches by learning a
prediction of the dynamic evolution of the robot centroidal momenta, which can
then be used for quickly generating dynamically robust contact sequences for
robots with arms and legs using a search-based contact planner. We demonstrate
the efficiency and quality of the results of the proposed approach in a set of
dynamically challenging scenarios
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