93 research outputs found
Exploiting Scene-specific Features for Object Goal Navigation
Can the intrinsic relation between an object and the room in which it is
usually located help agents in the Visual Navigation Task? We study this
question in the context of Object Navigation, a problem in which an agent has
to reach an object of a specific class while moving in a complex domestic
environment. In this paper, we introduce a new reduced dataset that speeds up
the training of navigation models, a notoriously complex task. Our proposed
dataset permits the training of models that do not exploit online-built maps in
reasonable times even without the use of huge computational resources.
Therefore, this reduced dataset guarantees a significant benchmark and it can
be used to identify promising models that could be then tried on bigger and
more challenging datasets. Subsequently, we propose the SMTSC model, an
attention-based model capable of exploiting the correlation between scenes and
objects contained in them, highlighting quantitatively how the idea is correct.Comment: Accepted at ACVR2020 ECCV2020 Worksho
Sampling-based reactive motion planning with temporal logic constraints and imperfect state information
© 2017, Springer International Publishing AG. This paper presents a method that allows mobile systems with uncertainty in motion and sensing to react to unknown environments while high-level specifications are satisfied. Although previous works have addressed the problem of synthesising controllers under uncertainty constraints and temporal logic specifications, reaction to dynamic environments has not been considered under this scenario. The method uses feedback-based information roadmaps (FIRMs) to break the curse of history associated with partially observable systems. A transition system is incrementally constructed based on the idea of FIRMs by adding nodes on the belief space. Then, a policy is found in the product Markov decision process created between the transition system and a Rabin automaton representing a linear temporal logic formula. The proposed solution allows the system to react to previously unknown elements in the environment. To achieve fast reaction time, a FIRM considering the probability of violating the specification in each transition is used to drive the system towards local targets or to avoid obstacles. The method is demonstrated with an illustrative example
Efficient Multi-Robot Motion Planning for Unlabeled Discs in Simple Polygons
We consider the following motion-planning problem: we are given unit
discs in a simple polygon with vertices, each at their own start position,
and we want to move the discs to a given set of target positions. Contrary
to the standard (labeled) version of the problem, each disc is allowed to be
moved to any target position, as long as in the end every target position is
occupied. We show that this unlabeled version of the problem can be solved in
time, assuming that the start and target positions are at
least some minimal distance from each other. This is in sharp contrast to the
standard (labeled) and more general multi-robot motion-planning problem for
discs moving in a simple polygon, which is known to be strongly NP-hard
Finding a needle in an exponential haystack: Discrete RRT for exploration of implicit roadmaps in multi-robot motion planning
We present a sampling-based framework for multi-robot motion planning which
combines an implicit representation of a roadmap with a novel approach for
pathfinding in geometrically embedded graphs tailored for our setting. Our
pathfinding algorithm, discrete-RRT (dRRT), is an adaptation of the celebrated
RRT algorithm for the discrete case of a graph, and it enables a rapid
exploration of the high-dimensional configuration space by carefully walking
through an implicit representation of a tensor product of roadmaps for the
individual robots. We demonstrate our approach experimentally on scenarios of
up to 60 degrees of freedom where our algorithm is faster by a factor of at
least ten when compared to existing algorithms that we are aware of.Comment: Kiril Solovey and Oren Salzman contributed equally to this pape
Sampling-based path planning for multi-robot systems with co-safe linear temporal logic specifications
© 2017, Springer International Publishing AG. This paper addresses the problem of path planning for multiple robots under high-level specifications given as syntactically co-safe linear temporal logic formulae. Most of the existing solutions use the notion of abstraction to obtain a discrete transition system that simulates the dynamics of the robot. Nevertheless, these solutions have poor scalability with the dimension of the configuration space of the robots. For problems with a single robot, sampling-based methods have been presented as a solution to alleviate this limitation. The proposed solution extends the idea of sampling methods to the multiple robot case. The method samples the configuration space of the robots to incrementally constructs a transition system that models the motion of all the robots as a group. This transition system is then combined with a Büchi automaton, representing the specification, in a Cartesian product. The product is updated with each expansion of the transition system until a solution is found. We also present a new algorithm that improves the performance of the proposed method by guiding the expansion of the transition system. The method is demonstrated with examples considering different number of robots and specifications
A Cordial Sync: Going Beyond Marginal Policies for Multi-Agent Embodied Tasks
Autonomous agents must learn to collaborate. It is not scalable to develop a
new centralized agent every time a task's difficulty outpaces a single agent's
abilities. While multi-agent collaboration research has flourished in
gridworld-like environments, relatively little work has considered visually
rich domains. Addressing this, we introduce the novel task FurnMove in which
agents work together to move a piece of furniture through a living room to a
goal. Unlike existing tasks, FurnMove requires agents to coordinate at every
timestep. We identify two challenges when training agents to complete FurnMove:
existing decentralized action sampling procedures do not permit expressive
joint action policies and, in tasks requiring close coordination, the number of
failed actions dominates successful actions. To confront these challenges we
introduce SYNC-policies (synchronize your actions coherently) and CORDIAL
(coordination loss). Using SYNC-policies and CORDIAL, our agents achieve a 58%
completion rate on FurnMove, an impressive absolute gain of 25 percentage
points over competitive decentralized baselines. Our dataset, code, and
pretrained models are available at https://unnat.github.io/cordial-sync .Comment: Accepted to ECCV 2020 (spotlight); Project page:
https://unnat.github.io/cordial-syn
SIMS: A Hybrid Method for Rapid Conformational Analysis
Proteins are at the root of many biological functions, often performing complex tasks as the result of large changes in their
structure. Describing the exact details of these conformational changes, however, remains a central challenge for
computational biology due the enormous computational requirements of the problem. This has engendered the
development of a rich variety of useful methods designed to answer specific questions at different levels of spatial,
temporal, and energetic resolution. These methods fall largely into two classes: physically accurate, but computationally
demanding methods and fast, approximate methods. We introduce here a new hybrid modeling tool, the Structured
Intuitive Move Selector (SIMS), designed to bridge the divide between these two classes, while allowing the benefits of both
to be seamlessly integrated into a single framework. This is achieved by applying a modern motion planning algorithm,
borrowed from the field of robotics, in tandem with a well-established protein modeling library. SIMS can combine precise
energy calculations with approximate or specialized conformational sampling routines to produce rapid, yet accurate,
analysis of the large-scale conformational variability of protein systems. Several key advancements are shown, including the
abstract use of generically defined moves (conformational sampling methods) and an expansive probabilistic
conformational exploration. We present three example problems that SIMS is applied to and demonstrate a rapid solution
for each. These include the automatic determination of ムムactiveメメ residues for the hinge-based system Cyanovirin-N,
exploring conformational changes involving long-range coordinated motion between non-sequential residues in Ribose-
Binding Protein, and the rapid discovery of a transient conformational state of Maltose-Binding Protein, previously only
determined by Molecular Dynamics. For all cases we provide energetic validations using well-established energy fields,
demonstrating this framework as a fast and accurate tool for the analysis of a wide range of protein flexibility problems
Rapid Sampling of Molecular Motions with Prior Information Constraints
Proteins are active, flexible machines that perform a range of different
functions. Innovative experimental approaches may now provide limited partial
information about conformational changes along motion pathways of proteins.
There is therefore a need for computational approaches that can efficiently
incorporate prior information into motion prediction schemes. In this paper, we
present PathRover, a general setup designed for the integration
of prior information into the motion planning algorithm of rapidly exploring
random trees (RRT). Each suggested motion pathway comprises a sequence of
low-energy clash-free conformations that satisfy an arbitrary number of prior
information constraints. These constraints can be derived from experimental data
or from expert intuition about the motion. The incorporation of prior
information is very straightforward and significantly narrows down the vast
search in the typically high-dimensional conformational space, leading to
dramatic reduction in running time. To allow the use of state-of-the-art energy
functions and conformational sampling, we have integrated this framework into
Rosetta, an accurate protocol for diverse types of structural modeling. The
suggested framework can serve as an effective complementary tool for molecular
dynamics, Normal Mode Analysis, and other prevalent techniques for predicting
motion in proteins. We applied our framework to three different model systems.
We show that a limited set of experimentally motivated constraints may
effectively bias the simulations toward diverse predicates in an outright
fashion, from distance constraints to enforcement of loop closure. In
particular, our analysis sheds light on mechanisms of protein domain swapping
and on the role of different residues in the motion
- …