185 research outputs found
ViNG: Learning Open-World Navigation with Visual Goals
We propose a learning-based navigation system for reaching visually indicated
goals and demonstrate this system on a real mobile robot platform. Learning
provides an appealing alternative to conventional methods for robotic
navigation: instead of reasoning about environments in terms of geometry and
maps, learning can enable a robot to learn about navigational affordances,
understand what types of obstacles are traversable (e.g., tall grass) or not
(e.g., walls), and generalize over patterns in the environment. However, unlike
conventional planning algorithms, it is harder to change the goal for a learned
policy during deployment. We propose a method for learning to navigate towards
a goal image of the desired destination. By combining a learned policy with a
topological graph constructed out of previously observed data, our system can
determine how to reach this visually indicated goal even in the presence of
variable appearance and lighting. Three key insights, waypoint proposal, graph
pruning and negative mining, enable our method to learn to navigate in
real-world environments using only offline data, a setting where prior methods
struggle. We instantiate our method on a real outdoor ground robot and show
that our system, which we call ViNG, outperforms previously-proposed methods
for goal-conditioned reinforcement learning, including other methods that
incorporate reinforcement learning and search. We also study how \sysName
generalizes to unseen environments and evaluate its ability to adapt to such an
environment with growing experience. Finally, we demonstrate ViNG on a number
of real-world applications, such as last-mile delivery and warehouse
inspection. We encourage the reader to visit the project website for videos of
our experiments and demonstrations sites.google.com/view/ving-robot.Comment: Presented at International Conference on Robotics and Automation
(ICRA) 202
Inverse Optimal Planning for Air Traffic Control
We envision a system that concisely describes the rules of air traffic
control, assists human operators and supports dense autonomous air traffic
around commercial airports. We develop a method to learn the rules of air
traffic control from real data as a cost function via maximum entropy inverse
reinforcement learning. This cost function is used as a penalty for a
search-based motion planning method that discretizes both the control and the
state space. We illustrate the methodology by showing that our approach can
learn to imitate the airport arrival routes and separation rules of dense
commercial air traffic. The resulting trajectories are shown to be safe,
feasible, and efficient
Energy-Aware, Collision-Free Information Gathering for Heterogeneous Robot Teams
This paper considers the problem of safely coordinating a team of
sensor-equipped robots to reduce uncertainty about a dynamical process, where
the objective trades off information gain and energy cost. Optimizing this
trade-off is desirable, but leads to a non-monotone objective function in the
set of robot trajectories. Therefore, common multi-robot planners based on
coordinate descent lose their performance guarantees. Furthermore, methods that
handle non-monotonicity lose their performance guarantees when subject to
inter-robot collision avoidance constraints. As it is desirable to retain both
the performance guarantee and safety guarantee, this work proposes a
hierarchical approach with a distributed planner that uses local search with a
worst-case performance guarantees and a decentralized controller based on
control barrier functions that ensures safety and encourages timely arrival at
sensing locations. Via extensive simulations, hardware-in-the-loop tests and
hardware experiments, we demonstrate that the proposed approach achieves a
better trade-off between sensing and energy cost than coordinate-descent-based
algorithms.Comment: To appear in Transactions on Robotics; 18 pages and 16 figures. arXiv
admin note: text overlap with arXiv:2101.1109
Rapid Exploration for Open-World Navigation with Latent Goal Models
We describe a robotic learning system for autonomous exploration and
navigation in diverse, open-world environments. At the core of our method is a
learned latent variable model of distances and actions, along with a
non-parametric topological memory of images. We use an information bottleneck
to regularize the learned policy, giving us (i) a compact visual representation
of goals, (ii) improved generalization capabilities, and (iii) a mechanism for
sampling feasible goals for exploration. Trained on a large offline dataset of
prior experience, the model acquires a representation of visual goals that is
robust to task-irrelevant distractors. We demonstrate our method on a mobile
ground robot in open-world exploration scenarios. Given an image of a goal that
is up to 80 meters away, our method leverages its representation to explore and
discover the goal in under 20 minutes, even amidst previously-unseen obstacles
and weather conditions. Please check out the project website for videos of our
experiments and information about the real-world dataset used at
https://sites.google.com/view/recon-robot.Comment: Accepted for presentation at 5th Annual Conference on Robot Learning
(CoRL 2021), London, UK as an Oral Talk. Project page and dataset release at
https://sites.google.com/view/recon-robo
Factories of the Future
Engineering; Industrial engineering; Production engineerin
Space station automation study: Automation requriements derived from space manufacturing concepts,volume 2
Automation reuirements were developed for two manufacturing concepts: (1) Gallium Arsenide Electroepitaxial Crystal Production and Wafer Manufacturing Facility, and (2) Gallium Arsenide VLSI Microelectronics Chip Processing Facility. A functional overview of the ultimate design concept incoporating the two manufacturing facilities on the space station are provided. The concepts were selected to facilitate an in-depth analysis of manufacturing automation requirements in the form of process mechanization, teleoperation and robotics, sensors, and artificial intelligence. While the cost-effectiveness of these facilities was not analyzed, both appear entirely feasible for the year 2000 timeframe
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