4,429 research outputs found
Using Centroidal Voronoi Tessellations to Scale Up the Multi-dimensional Archive of Phenotypic Elites Algorithm
The recently introduced Multi-dimensional Archive of Phenotypic Elites
(MAP-Elites) is an evolutionary algorithm capable of producing a large archive
of diverse, high-performing solutions in a single run. It works by discretizing
a continuous feature space into unique regions according to the desired
discretization per dimension. While simple, this algorithm has a main drawback:
it cannot scale to high-dimensional feature spaces since the number of regions
increase exponentially with the number of dimensions. In this paper, we address
this limitation by introducing a simple extension of MAP-Elites that has a
constant, pre-defined number of regions irrespective of the dimensionality of
the feature space. Our main insight is that methods from computational geometry
could partition a high-dimensional space into well-spread geometric regions. In
particular, our algorithm uses a centroidal Voronoi tessellation (CVT) to
divide the feature space into a desired number of regions; it then places every
generated individual in its closest region, replacing a less fit one if the
region is already occupied. We demonstrate the effectiveness of the new
"CVT-MAP-Elites" algorithm in high-dimensional feature spaces through
comparisons against MAP-Elites in maze navigation and hexapod locomotion tasks
IndoorSim-to-OutdoorReal: Learning to Navigate Outdoors without any Outdoor Experience
We present IndoorSim-to-OutdoorReal (I2O), an end-to-end learned visual
navigation approach, trained solely in simulated short-range indoor
environments, and demonstrates zero-shot sim-to-real transfer to the outdoors
for long-range navigation on the Spot robot. Our method uses zero real-world
experience (indoor or outdoor), and requires the simulator to model no
predominantly-outdoor phenomenon (sloped grounds, sidewalks, etc). The key to
I2O transfer is in providing the robot with additional context of the
environment (i.e., a satellite map, a rough sketch of a map by a human, etc.)
to guide the robot's navigation in the real-world. The provided context-maps do
not need to be accurate or complete -- real-world obstacles (e.g., trees,
bushes, pedestrians, etc.) are not drawn on the map, and openings are not
aligned with where they are in the real-world. Crucially, these inaccurate
context-maps provide a hint to the robot about a route to take to the goal. We
find that our method that leverages Context-Maps is able to successfully
navigate hundreds of meters in novel environments, avoiding novel obstacles on
its path, to a distant goal without a single collision or human intervention.
In comparison, policies without the additional context fail completely. Lastly,
we test the robustness of the Context-Map policy by adding varying degrees of
noise to the map in simulation. We find that the Context-Map policy is
surprisingly robust to noise in the provided context-map. In the presence of
significantly inaccurate maps (corrupted with 50% noise, or entirely blank
maps), the policy gracefully regresses to the behavior of a policy with no
context. Videos are available at https://www.joannetruong.com/projects/i2o.htm
An adaptive framework for 'single shot' motion planning
Due to the character of the original source materials and the nature of batch digitization, quality control issues may be present in this document. Please report any quality issues you encounter to [email protected], referencing the URI of the item.Includes bibliographical references (leaves 19-21).Automatic motion planning has applications in many areas such as robotics, virtual reality systems, and computer-aided design. Although many different motion planning methods have been proposed, most are not used in practice since they are computationally infeasible except for some restricted cases, e.g., when the robot has very few degrees of freedom (dof). For this reason, attention has focussed on randomized or probabilistic motion planning methods. When many motion planning queries will be performed in the same environment, then it may be useful to pre-process the environment with the goal of decreasing the difficulty of the subsequent queries. Examples are the roadmap motion planning methods, which build a graph encoding representative feasible paths (usually in the robot's configuration space, which is the parametric spacer representing all possible positions and orientations of the robot in the workspace). Indeed, recently several probabilistic roadmap methods (PRMs) (including our group's obstacle-based PRM ) have been used to solve many difficult planning problems involving high-dimensional C-spaces that could not be solved before. However, if the start and goal configurations are known a priori, only one (or a very few) queries will be performed in a single environment, then it is generally not worthwhile to perform an expensive preprocessing stage, particularly if there are time constraints as in animation or virtual reality applications. In this case, a more directed search of the free configuration space is needed (e.g., as opposed to roadmap methods which are designed to try to cover the entire freespace). Motion planning methods that operate in this fashion are often called single shot methods. In our current work, we are developing an adaptive framework for single shot motion planning (i.e., planning without preprocessing). This framework can be used in any situation, and in particular, is suitable for crowded environments in which the robot's free C-space has narrow corridors. The main idea of our framework is that one should adaptively select a planner whose strengths match the current situation, and then switch to a different planner when circumstances change. This approach requires that we develop a set of planners, and characterize the strengths and weaknesses of each planner in such a way that we can easily select the best planner for the current situation. Our experimental results show that adaptive selection of different planning methods enables the algorithms to be used in a cooperative manner to successfully solve queries that none of them would be able to solve on their own
UIVNAV: Underwater Information-driven Vision-based Navigation via Imitation Learning
Autonomous navigation in the underwater environment is challenging due to
limited visibility, dynamic changes, and the lack of a cost-efficient accurate
localization system. We introduce UIVNav, a novel end-to-end underwater
navigation solution designed to drive robots over Objects of Interest (OOI)
while avoiding obstacles, without relying on localization. UIVNav uses
imitation learning and is inspired by the navigation strategies used by human
divers who do not rely on localization. UIVNav consists of the following
phases: (1) generating an intermediate representation (IR), and (2) training
the navigation policy based on human-labeled IR. By training the navigation
policy on IR instead of raw data, the second phase is domain-invariant -- the
navigation policy does not need to be retrained if the domain or the OOI
changes. We show this by deploying the same navigation policy for surveying two
different OOIs, oyster and rock reefs, in two different domains, simulation,
and a real pool. We compared our method with complete coverage and random walk
methods which showed that our method is more efficient in gathering information
for OOIs while also avoiding obstacles. The results show that UIVNav chooses to
visit the areas with larger area sizes of oysters or rocks with no prior
information about the environment or localization. Moreover, a robot using
UIVNav compared to complete coverage method surveys on average 36% more oysters
when traveling the same distances. We also demonstrate the feasibility of
real-time deployment of UIVNavin pool experiments with BlueROV underwater robot
for surveying a bed of oyster shells
FastRLAP: A System for Learning High-Speed Driving via Deep RL and Autonomous Practicing
We present a system that enables an autonomous small-scale RC car to drive
aggressively from visual observations using reinforcement learning (RL). Our
system, FastRLAP (faster lap), trains autonomously in the real world, without
human interventions, and without requiring any simulation or expert
demonstrations. Our system integrates a number of important components to make
this possible: we initialize the representations for the RL policy and value
function from a large prior dataset of other robots navigating in other
environments (at low speed), which provides a navigation-relevant
representation. From here, a sample-efficient online RL method uses a single
low-speed user-provided demonstration to determine the desired driving course,
extracts a set of navigational checkpoints, and autonomously practices driving
through these checkpoints, resetting automatically on collision or failure.
Perhaps surprisingly, we find that with appropriate initialization and choice
of algorithm, our system can learn to drive over a variety of racing courses
with less than 20 minutes of online training. The resulting policies exhibit
emergent aggressive driving skills, such as timing braking and acceleration
around turns and avoiding areas which impede the robot's motion, approaching
the performance of a human driver using a similar first-person interface over
the course of training
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