205 research outputs found
Provably Safe Robot Navigation with Obstacle Uncertainty
As drones and autonomous cars become more widespread it is becoming
increasingly important that robots can operate safely under realistic
conditions. The noisy information fed into real systems means that robots must
use estimates of the environment to plan navigation. Efficiently guaranteeing
that the resulting motion plans are safe under these circumstances has proved
difficult. We examine how to guarantee that a trajectory or policy is safe with
only imperfect observations of the environment. We examine the implications of
various mathematical formalisms of safety and arrive at a mathematical notion
of safety of a long-term execution, even when conditioned on observational
information. We present efficient algorithms that can prove that trajectories
or policies are safe with much tighter bounds than in previous work. Notably,
the complexity of the environment does not affect our methods ability to
evaluate if a trajectory or policy is safe. We then use these safety checking
methods to design a safe variant of the RRT planning algorithm.Comment: RSS 201
Sampling-Based Methods for Factored Task and Motion Planning
This paper presents a general-purpose formulation of a large class of
discrete-time planning problems, with hybrid state and control-spaces, as
factored transition systems. Factoring allows state transitions to be described
as the intersection of several constraints each affecting a subset of the state
and control variables. Robotic manipulation problems with many movable objects
involve constraints that only affect several variables at a time and therefore
exhibit large amounts of factoring. We develop a theoretical framework for
solving factored transition systems with sampling-based algorithms. The
framework characterizes conditions on the submanifold in which solutions lie,
leading to a characterization of robust feasibility that incorporates
dimensionality-reducing constraints. It then connects those conditions to
corresponding conditional samplers that can be composed to produce values on
this submanifold. We present two domain-independent, probabilistically complete
planning algorithms that take, as input, a set of conditional samplers. We
demonstrate the empirical efficiency of these algorithms on a set of
challenging task and motion planning problems involving picking, placing, and
pushing
PDDLStream: Integrating Symbolic Planners and Blackbox Samplers via Optimistic Adaptive Planning
Many planning applications involve complex relationships defined on
high-dimensional, continuous variables. For example, robotic manipulation
requires planning with kinematic, collision, visibility, and motion constraints
involving robot configurations, object poses, and robot trajectories. These
constraints typically require specialized procedures to sample satisfying
values. We extend PDDL to support a generic, declarative specification for
these procedures that treats their implementation as black boxes. We provide
domain-independent algorithms that reduce PDDLStream problems to a sequence of
finite PDDL problems. We also introduce an algorithm that dynamically balances
exploring new candidate plans and exploiting existing ones. This enables the
algorithm to greedily search the space of parameter bindings to more quickly
solve tightly-constrained problems as well as locally optimize to produce
low-cost solutions. We evaluate our algorithms on three simulated robotic
planning domains as well as several real-world robotic tasks.Comment: International Conference on Automated Planning and Scheduling (ICAPS)
202
Protein side-chain placement: probabilistic inference and integer programming methods
The prediction of energetically favorable side-chain conformations is a fundamental element in homology modeling of proteins and the design of novel protein sequences. The space of side-chain conformations can be approximated by a discrete space of probabilistically representative side-chain conformations (called rotamers). The problem is, then, to find a rotamer selection for each amino acid that minimizes a potential energy function. This is called the Global Minimum Energy Conformation (GMEC) problem. This problem is an NP-hard optimization problem. The Dead-End Elimination theorem together with the A* algorithm (DEE/A*) has been successfully applied to this problem. However, DEE fails to converge for some complex instances. In this paper, we explore two alternatives to DEE/A* in solving the GMEC problem. We use a probabilistic inference method, the max-product (MP) belief-propagation algorithm, to estimate (often exactly) the GMEC. We also investigate integer programming formulations to obtain the exact solution. There are known ILP formulations that can be directly applied to the GMEC problem. We review these formulations and compare their effectiveness using CPLEX optimizers. We also present preliminary work towards applying the branch-and-price approach to the GMEC problem. The preliminary results suggest that the max-product algorithm is very effective for the GMEC problem. Though the max-product algorithm is an approximate method, its speed and accuracy are comparable to those of DEE/A* in large side-chain placement problems and may be superior in sequence design.Singapore-MIT Alliance (SMA
OmniMerge: A Systematic Approach to Constrained Conformational Search
OmniMerge performs a systematic search to enumerate all conformations of a molecule (at a given level of torsion-angle resolution) that satisfy a set of local geometric constraints. Constraints would typically come from NMR experiments, but applications such as docking or homology modeling could also give rise to similar constraints. The molecule to be searched is partitioned into small subchains so that the set of possible conformations for the whole molecule may be constructed by merging the feasible conformations for the subchain parts. However, instead of using a binary tree for straightforward divide-and-conquer, OmniMerge defines a sub-problem for every possible subchain of the molecule. Searching every subchain provides a counter-intuitive advantage: with every possible subdivision available for merging, one may choose the most favorable merge for each subchain, particularly for the bottleneck chain(s). Improving the bottleneck step may therefore cause the whole search to be completed more quickly. Finally, to discard infeasible conformations more rapidly, OmniMerge filters the solution set of each subchain based on compatibility with the solutions sets of all overlapping subchains. These two innovations—choosing the most favorable merges and enforcing consistency between overlapping subchains—yield significant improvements in run time. By determining the extent of structural variability permitted by a set of constraints, OmniMerge offers the potential to aid error analysis and improve confidence for NMR results on peptides and moderate-sized molecules.Singapore-MIT Alliance (SMA
Matching Interest Points Using Projective Invariant Concentric Circles
We present a new method to perform reliable matching between different images. This method exploits a projective invariant property between concentric circles and the corresponding projected ellipses to find complete region correspondences centered on interest points. The method matches interest points allowing for a full perspective transformation and exploiting all the available luminance information in the regions. Experiments have been conducted on many different data sets to compare our approach to SIFT local descriptors. The results show the new method offers increased robustness to partial visibility, object rotation in depth, and viewpoint angle change.Singapore-MIT Alliance (SMA
Active model learning and diverse action sampling for task and motion planning
The objective of this work is to augment the basic abilities of a robot by
learning to use new sensorimotor primitives to enable the solution of complex
long-horizon problems. Solving long-horizon problems in complex domains
requires flexible generative planning that can combine primitive abilities in
novel combinations to solve problems as they arise in the world. In order to
plan to combine primitive actions, we must have models of the preconditions and
effects of those actions: under what circumstances will executing this
primitive achieve some particular effect in the world?
We use, and develop novel improvements on, state-of-the-art methods for
active learning and sampling. We use Gaussian process methods for learning the
conditions of operator effectiveness from small numbers of expensive training
examples collected by experimentation on a robot. We develop adaptive sampling
methods for generating diverse elements of continuous sets (such as robot
configurations and object poses) during planning for solving a new task, so
that planning is as efficient as possible. We demonstrate these methods in an
integrated system, combining newly learned models with an efficient
continuous-space robot task and motion planner to learn to solve long horizon
problems more efficiently than was previously possible.Comment: Proceedings of the 2018 IEEE/RSJ International Conference on
Intelligent Robots and Systems (IROS), Madrid, Spain.
https://www.youtube.com/playlist?list=PLoWhBFPMfSzDbc8CYelsbHZa1d3uz-W_
Learning Three-Dimensional Shape Models for Sketch Recognition
Artifacts made by humans, such as items of furniture and houses, exhibit an enormous amount of variability in shape. In this paper, we concentrate on models of the shapes of objects that are made up of fixed collections of sub-parts whose dimensions and spatial arrangement exhibit variation. Our goals are: to learn these models from data and to use them for recognition. Our emphasis is on learning and recognition from three-dimensional data, to test the basic shape-modeling methodology. In this paper we also demonstrate how to use models learned in three dimensions for recognition of two-dimensional sketches of objects.Singapore-MIT Alliance (SMA
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