1,138 research outputs found
Role Playing Learning for Socially Concomitant Mobile Robot Navigation
In this paper, we present the Role Playing Learning (RPL) scheme for a mobile
robot to navigate socially with its human companion in populated environments.
Neural networks (NN) are constructed to parameterize a stochastic policy that
directly maps sensory data collected by the robot to its velocity outputs,
while respecting a set of social norms. An efficient simulative learning
environment is built with maps and pedestrians trajectories collected from a
number of real-world crowd data sets. In each learning iteration, a robot
equipped with the NN policy is created virtually in the learning environment to
play itself as a companied pedestrian and navigate towards a goal in a socially
concomitant manner. Thus, we call this process Role Playing Learning, which is
formulated under a reinforcement learning (RL) framework. The NN policy is
optimized end-to-end using Trust Region Policy Optimization (TRPO), with
consideration of the imperfectness of robot's sensor measurements. Simulative
and experimental results are provided to demonstrate the efficacy and
superiority of our method
Output feedback NN control for two classes of discrete-time systems with unknown control directions in a unified approach
10.1109/TNN.2008.2003290IEEE Transactions on Neural Networks19111873-1886ITNN
Reference adaptation for robots in physical interactions with unknown environments
In this paper, we propose a method of reference adaptation for robots in physical interactions with unknown environments. A cost function is constructed to describe the interaction performance, which combines trajectory tracking error and interaction force between the robot and the environment. It is minimized by the proposed reference adaptation based on trajectory parametrization and iterative learning. An adaptive impedance control is developed to make the robot be governed by the target impedance model. Simulation and experiment studies are conducted to verify the effectiveness of the proposed method
Adaptive control for robot navigation in human environments based on social force model
In this paper, we introduce a novel control scheme based on the social force model for robots navigating in human environments. Social proxemics potential field is constructed based on the theory of proxemics and used to generate social interaction force for design of robot motion control. A combined kinematic/dynamic control is proposed to make the robot follow the target social force model, in the presence of kinematic velocity constraints. Under the proposed framework, given a specific social convention, robot is able to generate and modify its path smoothly without violating the proxemics constraints. The validity of the proposed method is verified through experimental studies using the V-rep platform
Optimal critic learning for robot control in time-varying environments
In this paper, optimal critic learning is developed for robot control in a time-varying environment. The unknown environment is described as a linear system with time-varying parameters, and impedance control is employed for the interaction control. Desired impedance parameters are obtained in the sense of an optimal realization of the composite of trajectory tracking and force regulation. Q-function based critic learning is developed to determine the optimal impedance parameters without the knowledge of the system dynamics. Simulation results are presented and compared with existing methods, and the efficacy of the proposed method is verified
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Adaptive control of robotic manipulators with unified motion constraints
In this paper, we present an adaptive control of robotic manipulators with parametric uncertainties and motion constraints. Position and velocity constraints are considered and they are unified and converted into the constraint of the nominal input. An adaptive neural network control is developed to achieve trajectory tracking, while the problems of motion constraints are addressed by considering the saturation effect of the nominal input. The uniform boundedness of all closed-loop signals is verified through Lyapunov analysis. Simulation and experiment results on a 2 DOF robotic manipulator demonstrate the effectiveness of the proposed method
Alternating Direction Method of Multipliers for Constrained Iterative LQR in Autonomous Driving
In the context of autonomous driving, the iterative linear quadratic
regulator (iLQR) is known to be an efficient approach to deal with the
nonlinear vehicle models in motion planning problems. Particularly, the
constrained iLQR algorithm has shown noteworthy advantageous outcomes of
computation efficiency in achieving motion planning tasks under general
constraints of different types. However, the constrained iLQR methodology
requires a feasible trajectory at the first iteration as a prerequisite. Also,
the methodology leaves open the possibility for incorporation of fast,
efficient, and effective optimization methods (i.e., fast-solvers) to further
speed up the optimization process such that the requirements of real-time
implementation can be successfully fulfilled. In this paper, a well-defined and
commonly-encountered motion planning problem is formulated under nonlinear
vehicle dynamics and various constraints, and an alternating direction method
of multipliers (ADMM) is developed to determine the optimal control actions.
With this development, the approach is able to circumvent the feasibility
requirement of the trajectory at the first iteration. An illustrative example
of motion planning in autonomous vehicles is then investigated with different
driving scenarios taken into consideration. As clearly observed from the
simulation results, the significance of this work in terms of obstacle
avoidance is demonstrated. Furthermore, a noteworthy achievement of high
computation efficiency is attained; and as a result, real-time computation and
implementation can be realized through this framework, and thus it provides
additional safety to the on-road driving tasks.Comment: 9 pages, 8 figure
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