8,722 research outputs found
Motion Planning of Uncertain Ordinary Differential Equation Systems
This work presents a novel motion planning framework, rooted in nonlinear programming theory, that treats uncertain fully and under-actuated dynamical systems described by ordinary differential equations. Uncertainty in multibody dynamical systems comes from various sources, such as: system parameters, initial conditions, sensor and actuator noise, and external forcing. Treatment of uncertainty in design is of paramount practical importance because all real-life systems are affected by it, and poor robustness and suboptimal performance result if it’s not accounted for in a given design. In this work uncertainties are modeled using Generalized Polynomial Chaos and are solved quantitatively using a least-square collocation method. The computational efficiency of this approach enables the inclusion of uncertainty statistics in the nonlinear programming optimization process. As such, the proposed framework allows the user to pose, and answer, new design questions related to uncertain dynamical systems.
Specifically, the new framework is explained in the context of forward, inverse, and hybrid dynamics formulations. The forward dynamics formulation, applicable to both fully and under-actuated systems, prescribes deterministic actuator inputs which yield uncertain state trajectories. The inverse dynamics formulation is the dual to the forward dynamic, and is only applicable to fully-actuated systems; deterministic state trajectories are prescribed and yield uncertain actuator inputs. The inverse dynamics formulation is more computationally efficient as it requires only algebraic evaluations and completely avoids numerical integration. Finally, the hybrid dynamics formulation is applicable to under-actuated systems where it leverages the benefits of inverse dynamics for actuated joints and forward dynamics for unactuated joints; it prescribes actuated state and unactuated input trajectories which yield uncertain unactuated states and actuated inputs.
The benefits of the ability to quantify uncertainty when planning the motion of multibody dynamic systems are illustrated through several case-studies. The resulting designs determine optimal motion plans—subject to deterministic and statistical constraints—for all possible systems within the probability space
Projective simulation for artificial intelligence
We propose a model of a learning agent whose interaction with the environment
is governed by a simulation-based projection, which allows the agent to project
itself into future situations before it takes real action. Projective
simulation is based on a random walk through a network of clips, which are
elementary patches of episodic memory. The network of clips changes
dynamically, both due to new perceptual input and due to certain compositional
principles of the simulation process. During simulation, the clips are screened
for specific features which trigger factual action of the agent. The scheme is
different from other, computational, notions of simulation, and it provides a
new element in an embodied cognitive science approach to intelligent action and
learning. Our model provides a natural route for generalization to
quantum-mechanical operation and connects the fields of reinforcement learning
and quantum computation.Comment: 22 pages, 18 figures. Close to published version, with footnotes
retaine
Combining Physical Simulators and Object-Based Networks for Control
Physics engines play an important role in robot planning and control;
however, many real-world control problems involve complex contact dynamics that
cannot be characterized analytically. Most physics engines therefore employ .
approximations that lead to a loss in precision. In this paper, we propose a
hybrid dynamics model, simulator-augmented interaction networks (SAIN),
combining a physics engine with an object-based neural network for dynamics
modeling. Compared with existing models that are purely analytical or purely
data-driven, our hybrid model captures the dynamics of interacting objects in a
more accurate and data-efficient manner.Experiments both in simulation and on a
real robot suggest that it also leads to better performance when used in
complex control tasks. Finally, we show that our model generalizes to novel
environments with varying object shapes and materials.Comment: ICRA 2019; Project page: http://sain.csail.mit.ed
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
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