Navigation of uncertain terrain by fusion of information from real and synthetic imagery

Lyons, Damian M.; Nirmal, Paramesh; Benjamin, D. Paul

doi:10.1117/12.919330

Cited by 6 publications

(5 citation statements)

References 15 publications

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“…For robots, through inner rehearsal, the result of a motion command can be predicted without actual execution [32]. Inner rehearsal has been used in robot linguistic interpretation [43], relation learning [44], and navigation [45] to predict the result of a command and choose an action accordingly. In this way, robots can avoid unnecessary attempts and conduct the best move.…”

Section: Inner Rehearsalmentioning

confidence: 99%

Robot Arm Reaching Based on Inner Rehearsal

Wang,

Zou,

Wei

et al. 2023

Biomimetics

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Robot arm motion control is a fundamental aspect of robot capabilities, with arm reaching ability serving as the foundation for complex arm manipulation tasks. However, traditional inverse kinematics-based methods for robot arm reaching struggle to cope with the increasing complexity and diversity of robot environments, as they heavily rely on the accuracy of physical models. In this paper, we introduce an innovative approach to robot arm motion control, inspired by the cognitive mechanism of inner rehearsal observed in humans. The core concept revolves around the robot’s ability to predict or evaluate the outcomes of motion commands before execution. This approach enhances the learning efficiency of models and reduces the mechanical wear on robots caused by excessive physical executions. We conduct experiments using the Baxter robot in simulation and the humanoid robot PKU-HR6.0 II in a real environment to demonstrate the effectiveness and efficiency of our proposed approach for robot arm reaching across different platforms. The internal models converge quickly and the average error distance between the target and the end-effector on the two platforms is reduced by 80% and 38%, respectively.

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Section: Inner Rehearsalmentioning

confidence: 99%

Robot Arm Reaching Based on Inner Rehearsal

Wang,

Zou,

Wei

et al. 2023

Biomimetics

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“…Addressing the problem of making a robot simulation produce more realistic behavior does not just concern improved physics engine and rendering technology, but also concerns handling uncertainty about which specific environment the robot will encounter. Benjamin [6] and Lyons [7] proposed an approach for cognitive robotics where a simulation is used to predict the environment state and to visually compare video with simulation predicted imagery. Differences are used to modify the simulation, changing object appearance, positions and velocities to match the observed video.…”

Section: Related Workmentioning

confidence: 99%

“…Our proposed approach is most like that of Chebotar. However, instead of using the domain randomization -establishing a range of initial parameter values -we follow an approach more similar to Benjamin and Lyons [7] who modify simulation state during a simulation to close the reality gap. Our approach differs from sim-to-real approaches in putting the emphasis on improving the simulation so that the sim-to-real step becomes less onerous.…”

Section: Related Workmentioning

confidence: 99%

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A Monte Carlo Approach to Closing the Reality Gap

Lyons,

Finocchiaro,

Novitzky

et al. 2020

Preprint

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We propose a novel approach to the 'reality gap' problem, i.e., modifying a robot simulation so that its performance becomes more similar to observed real world phenomena. This problem arises whether the simulation is being used by human designers or in an automated policy development mechanism. We expect that the program/policy is developed using simulation, and subsequently deployed on a real system. We further assume that the program includes a monitor procedure with scalar output to determine when it is achieving its performance objectives. The proposed approach collects simulation and real world observations and builds conditional probability functions. These are used to generate paired roll-outs to identify points of divergence in behavior. These are used to generate statespace kernels that coerce the simulation into behaving more like observed reality.The method was evaluated using ROS/Gazebo for simulation and a heavily modified Traaxas platform in outdoor deployment. The results support not just that the kernel approach can force the simulation to behave more like reality, but that the modification is such that an improved control policy tested in the modified simulation also performs better in the real world.

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“…Lyons et al . presented an autonomous platform that searches or traverses a building to observe unstable masonry or rubble [26]. In these situations, a reactive approach may produce behaviour that works against the robots' long-term goals, such as taking the quickest or safest route to a disaster victim.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid System of Hierarchical Planning of Behaviour Selection Networks for Mobile Robot Control

Lee

Cho

2014

International Journal of Advanced Robotic Systems

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An office delivery robot receives a large amount of sensory data and there is uncertainty in its action outcomes. The robot should not only accomplish its goals using environmental information, but also consider various exceptions simultaneously. In this paper, we propose a hybrid system using hierarchical planning of modular behaviour selection networks to generate autonomous behaviour in the office delivery robot. Behaviour selection networks, one of the well-known behaviour-based methods suitable for goal-oriented tasks, are made up of several smaller behaviour modules. Planning is attached to the construct and adjust sequences of the modules by considering the sub-goals, the priority in each task and the user feedback. This helps the robot to quickly react in dynamic situations as well as achieve global goals efficiently. The proposed system is verified with both the Webot simulator and a Khepera II robot that runs in a real office environment carrying out delivery tasks. Experimental results have shown that a robot can achieve goals and generate module sequences successfully even in unpredictable situations. Additionally, the proposed planning method reduced the elapsed time during tasks by 17.5% since it adjusts the behaviour module sequences more effectively.

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Navigation of uncertain terrain by fusion of information from real and synthetic imagery

Cited by 6 publications

References 15 publications

Robot Arm Reaching Based on Inner Rehearsal

Robot Arm Reaching Based on Inner Rehearsal

A Monte Carlo Approach to Closing the Reality Gap

A Hybrid System of Hierarchical Planning of Behaviour Selection Networks for Mobile Robot Control

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