7,356 research outputs found
Learning Models for Following Natural Language Directions in Unknown Environments
Natural language offers an intuitive and flexible means for humans to
communicate with the robots that we will increasingly work alongside in our
homes and workplaces. Recent advancements have given rise to robots that are
able to interpret natural language manipulation and navigation commands, but
these methods require a prior map of the robot's environment. In this paper, we
propose a novel learning framework that enables robots to successfully follow
natural language route directions without any previous knowledge of the
environment. The algorithm utilizes spatial and semantic information that the
human conveys through the command to learn a distribution over the metric and
semantic properties of spatially extended environments. Our method uses this
distribution in place of the latent world model and interprets the natural
language instruction as a distribution over the intended behavior. A novel
belief space planner reasons directly over the map and behavior distributions
to solve for a policy using imitation learning. We evaluate our framework on a
voice-commandable wheelchair. The results demonstrate that by learning and
performing inference over a latent environment model, the algorithm is able to
successfully follow natural language route directions within novel, extended
environments.Comment: ICRA 201
Toward Specification-Guided Active Mars Exploration for Cooperative Robot Teams
As a step towards achieving autonomy in space exploration missions, we consider a cooperative robotics system consisting of a copter and a rover. The goal of the copter is to explore an unknown environment so as to maximize knowledge about a science mission expressed in linear temporal logic that is to be executed by the rover. We model environmental uncertainty as a belief space Markov decision process and formulate the problem as a two-step stochastic dynamic program that we solve in a way that leverages the decomposed nature of the overall system. We demonstrate in simulations that the robot team makes intelligent decisions in the face of uncertainty
Learning Augmented, Multi-Robot Long-Horizon Navigation in Partially Mapped Environments
We present a novel approach for efficient and reliable goal-directed
long-horizon navigation for a multi-robot team in a structured, unknown
environment by predicting statistics of unknown space. Building on recent work
in learning-augmented model based planning under uncertainty, we introduce a
high-level state and action abstraction that lets us approximate the
challenging Dec-POMDP into a tractable stochastic MDP. Our Multi-Robot Learning
over Subgoals Planner (MR-LSP) guides agents towards coordinated exploration of
regions more likely to reach the unseen goal. We demonstrate improvement in
cost against other multi-robot strategies; in simulated office-like
environments, we show that our approach saves 13.29% (2 robot) and 4.6% (3
robot) average cost versus standard non-learned optimistic planning and a
learning-informed baseline.Comment: 7 pages, 7 figures, ICRA202
Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
Simultaneous Localization and Mapping (SLAM)consists in the concurrent
construction of a model of the environment (the map), and the estimation of the
state of the robot moving within it. The SLAM community has made astonishing
progress over the last 30 years, enabling large-scale real-world applications,
and witnessing a steady transition of this technology to industry. We survey
the current state of SLAM. We start by presenting what is now the de-facto
standard formulation for SLAM. We then review related work, covering a broad
set of topics including robustness and scalability in long-term mapping, metric
and semantic representations for mapping, theoretical performance guarantees,
active SLAM and exploration, and other new frontiers. This paper simultaneously
serves as a position paper and tutorial to those who are users of SLAM. By
looking at the published research with a critical eye, we delineate open
challenges and new research issues, that still deserve careful scientific
investigation. The paper also contains the authors' take on two questions that
often animate discussions during robotics conferences: Do robots need SLAM? and
Is SLAM solved
Autonomous 3D Exploration of Large Structures Using an UAV Equipped with a 2D LIDAR
This paper addressed the challenge of exploring large, unknown, and unstructured
industrial environments with an unmanned aerial vehicle (UAV). The resulting system combined
well-known components and techniques with a new manoeuvre to use a low-cost 2D laser to measure
a 3D structure. Our approach combined frontier-based exploration, the Lazy Theta* path planner, and
a flyby sampling manoeuvre to create a 3D map of large scenarios. One of the novelties of our system
is that all the algorithms relied on the multi-resolution of the octomap for the world representation.
We used a Hardware-in-the-Loop (HitL) simulation environment to collect accurate measurements
of the capability of the open-source system to run online and on-board the UAV in real-time. Our
approach is compared to different reference heuristics under this simulation environment showing
better performance in regards to the amount of explored space. With the proposed approach, the UAV
is able to explore 93% of the search space under 30 min, generating a path without repetition that
adjusts to the occupied space covering indoor locations, irregular structures, and suspended obstaclesUnión Europea Marie Sklodowska-Curie 64215Unión Europea MULTIDRONE (H2020-ICT-731667)Uniión Europea HYFLIERS (H2020-ICT-779411
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