Belief Space Planning under Approximate Hybrid Dynamics
نویسندگان
چکیده
The difficulty of many robot controls tasks stems from stochasticity and partial observability coupled with highly nonlinear dynamics. We propose to approximate nonlinear system dynamics using hybrid dynamics models and extend the POMDP framework to hybrid systems. To do this, we introduce a Bayesian inference based hybrid state evolution model that can be used to develop feasible motion plans under partial observability.
منابع مشابه
Motion Planning Under Uncertainty Using Differential Dynamic Programming in Belief Space
We present an approach to motion planning under motion and sensing uncertainty, formally described as a continuous partially-observable Markov decision process (POMDP). Our approach is designed for non-linear dynamics and observation models, and follows the general POMDP solution framework in which we represent beliefs by Gaussian distributions, approximate the belief dynamics using an extended...
متن کاملMotion planning under uncertainty using iterative local optimization in belief space
We present a new approach to motion planning under sensing and motion uncertainty by computing a locally optimal solution to a continuous partially observable Markov decision process (POMDP). Our approach represents beliefs (the distributions of the robot’s state estimate) by Gaussian distributions and is applicable to robot systems with non-linear dynamics and observation models. The method fo...
متن کاملIntelligence in the Now: Robust Intelligence in Complex Domains
Our overall goal is to develop the estimation, planning, and control techniques necessary to enable robots to perform robustly and intelligently in complex uncertain domains. Robots operating in complex, unknown environments have to deal explicitly with uncertainty. Sensing is increasingly reliable, but inescapably local: robots cannot see, immediately, inside cupboards, under collapsed walls, ...
متن کاملWhat makes some POMDP problems easy to approximate?
Point-based algorithms have been surprisingly successful in computing approximately optimal solutions for partially observable Markov decision processes (POMDPs) in high dimensional belief spaces. In this work, we seek to understand the belief-space properties that allow some POMDP problems to be approximated efficiently and thus help to explain the point-based algorithms’ success often observe...
متن کاملKalman Based Temporal Difference Neural Network for Policy Generation under Uncertainty (KBTDNN)
A real world environment is often partially observable by the agents either because of noisy sensors or incomplete perception. Moreover, it has continuous state space in nature, and agents must decide on an action for each point in internal continuous belief space. Consequently, it is convenient to model this type of decision-making problems as Partially Observable Markov Decision Processes (PO...
متن کامل