Towards Reactive Control of Transitional Legged Robot Maneuvers

Duperret, Jeffrey; Koditschek, Daniel E.

doi:10.1007/978-3-030-28619-4_17

Cited by 2 publications

(2 citation statements)

References 24 publications

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“…Given a high-level discrete controller encoding reactive task behaviors, the work in DeCastro and Kress-Gazit (2015) designed low-level controllers to guarantee the correctness of a high-level controller. More recently, the work of Duperret and Koditschek (2020) solves a formal discrete leaping navigation problem of legged robots to reach a goal set while in the interim reactively avoiding a set of obstacle states. However, all of the work above is applied to 2D-world mobile robots or a single-leg hopper, which have simple dynamics unlike our focus on underactuated and hybrid legged robots.…”

Section: Related Workmentioning

confidence: 99%

Reactive task and motion planning for robust whole-body dynamic locomotion in constrained environments

Zhao

Sentis

et al. 2022

The International Journal of Robotics Research

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Contact-based decision and planning methods are becoming increasingly important to endow higher levels of autonomy for legged robots. Formal synthesis methods derived from symbolic systems have great potential for reasoning about high-level locomotion decisions and achieving complex maneuvering behaviors with correctness guarantees. This study takes a first step toward formally devising an architecture composed of task planning and control of whole-body dynamic locomotion behaviors in constrained and dynamically changing environments. At the high level, we formulate a two-player temporal logic game between the multi-limb locomotion planner and its dynamic environment to synthesize a winning strategy that delivers symbolic locomotion actions. These locomotion actions satisfy the desired high-level task specifications expressed in a fragment of temporal logic. Those actions are sent to a robust finite transition system that synthesizes a locomotion controller that fulfills state reachability constraints. This controller is further executed via a low-level motion planner that generates feasible locomotion trajectories. We construct a set of dynamic locomotion models for legged robots to serve as a template library for handling diverse environmental events. We devise a replanning strategy that takes into consideration sudden environmental changes or large state disturbances to increase the robustness of the resulting locomotion behaviors. We formally prove the correctness of the layered locomotion framework guaranteeing a robust implementation by the motion planning layer. Simulations of reactive locomotion behaviors in diverse environments indicate that our framework has the potential to serve as a theoretical foundation for intelligent locomotion behaviors.

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Section: Related Workmentioning

confidence: 99%

Reactive task and motion planning for robust whole-body dynamic locomotion in constrained environments

Zhao

Sentis

et al. 2022

The International Journal of Robotics Research

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“…The evolution of mobile robotics ensures the creation of technology for automating everyday tasks [1], from robots for domestic use, as presented in [2], often based on reactive control [3], to autonomous land vehicles [4], air vehicles [5] or water vehicles [6] and self-guided vehicles in Industry 4.0 [7], based on advanced control and automation techniques or artificial intelligence. The fundamental task performed by an autonomous vehicle, regardless of the environment in which it operates, is the ability to move around [8], whether in a controlled or an uncontrolled environment.…”

Section: Introductionmentioning

confidence: 99%

Trajectory Simulation Approach for Autonomous Vehicles Path Planning using Deep Reinforcement Learning

Lima

Oliveira

Costa

2020

Int J Innov Educ Res

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Autonomous vehicle path planning aims to allow safe and rapid movement in an environment without human interference. Recently, Reinforcement Learning methods have been used to solve this problem and have achieved satisfactory results. This work presents the use of Deep Reinforcement Learning for the task of path planning for autonomous vehicles through trajectory simulation, to define routes that offer greater safety (without collisions) and less distance for the displacement between two points. A method for creating simulation environments was developed to analyze the performance of the proposed models in different difficult degrees of circumstances. The decision-making strategy implemented was based on the use of Artificial Neural Networks of the Multilayer Perceptron type with parameters and hyperparameters determined from a grid search. The models were evaluated for their reward charts resulting from their learning process. Such evaluation occurred in two phases: isolated evaluation, in which the models were inserted into the environment without prior knowledge; and incremental evaluation, in which models were inserted in unknown environments with previous intelligence accumulated in other conditions. The results obtained are competitive with state-of-the-art works and highlight the adaptive characteristic of the models presented, which, when inserted with prior knowledge in environments, can reduce the convergence time by up to 89.47% when compared to related works.

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Towards Reactive Control of Transitional Legged Robot Maneuvers

Cited by 2 publications

References 24 publications

Reactive task and motion planning for robust whole-body dynamic locomotion in constrained environments

Reactive task and motion planning for robust whole-body dynamic locomotion in constrained environments

Trajectory Simulation Approach for Autonomous Vehicles Path Planning using Deep Reinforcement Learning

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