An Extended Convergence Result for Behaviour Tree Controllers

• URL: http://arxiv.org/abs/2308.08994v1
• Date: Thu, 17 Aug 2023 14:05:45 GMT
• Title: An Extended Convergence Result for Behaviour Tree Controllers
• Authors: Christopher Iliffe Sprague, Petter \"Ogren
• Abstract summary: Behavior trees (BTs) are an optimally modular framework to assemble hierarchical hybrid control policies. We study the convergence of BTs, in the sense of reaching a desired part of the state space.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Behavior trees (BTs) are an optimally modular framework to assemble hierarchical hybrid control policies from a set of low-level control policies using a tree structure. Many robotic tasks are naturally decomposed into a hierarchy of control tasks, and modularity is a well-known tool for handling complexity, therefor behavior trees have garnered widespread usage in the robotics community. In this paper, we study the convergence of BTs, in the sense of reaching a desired part of the state space. Earlier results on BT convergence were often tailored to specific families of BTs, created using different design principles. The results of this paper generalize the earlier results and also include new cases of cyclic switching not covered in the literature.

Related papers

• Verifying Safety of Behaviour Trees in Event-B [0.0]
  Behavior Trees (BT) are becoming increasingly popular in the robotics community. We propose a formal specification of Behavior Trees and a methodology to prove invariants of already used trees.
  arXiv  Detail & Related papers  (2022-09-28T12:26:50Z)
• SoftTreeMax: Policy Gradient with Tree Search [72.9513807133171]
  We introduce SoftTreeMax, the first approach that integrates tree-search into policy gradient. On Atari, SoftTreeMax demonstrates up to 5x better performance in faster run-time compared with distributed PPO.
  arXiv  Detail & Related papers  (2022-09-28T09:55:47Z)
• Behavior Trees in Robot Control Systems [0.0]
  Key idea underlying behavior trees is to make use of modularity, hierarchies and feedback. A hierarchy of such modules is natural, since robot tasks can often be decomposed into a hierarchy of sub-tasks. feedback control is a fundamental tool for handling uncertainties and disturbances in any low level control system.
  arXiv  Detail & Related papers  (2022-03-24T14:16:15Z)
• Provable Hierarchy-Based Meta-Reinforcement Learning [50.17896588738377]
  We analyze HRL in the meta-RL setting, where learner learns latent hierarchical structure during meta-training for use in a downstream task. We provide "diversity conditions" which, together with a tractable optimism-based algorithm, guarantee sample-efficient recovery of this natural hierarchy. Our bounds incorporate common notions in HRL literature such as temporal and state/action abstractions, suggesting that our setting and analysis capture important features of HRL in practice.
  arXiv  Detail & Related papers  (2021-10-18T17:56:02Z)
• Continuous-Time Behavior Trees as Discontinuous Dynamical Systems [0.0]
  Behavior trees combine several low-level control policies into a high-level task-switching policy. A formal continuous-time formulation of behavior trees has been lacking. We show that behavior trees can be seen as discontinuous dynamical systems.
  arXiv  Detail & Related papers  (2021-09-03T15:11:42Z)
• An expressiveness hierarchy of Behavior Trees and related architectures [0.0]
  We provide a formal framework for comparing the expressive power of Behavior Trees (BTs) to other action selection architectures. This leads us to an hierarchy of control architectures, which includes BTs, Decision Trees (DTs), Teleo-reactive Programs (TRs) and Finite State Machines (FSMs)
  arXiv  Detail & Related papers  (2021-04-16T06:46:52Z)
• Convex Polytope Trees [57.56078843831244]
  convex polytope trees (CPT) are proposed to expand the family of decision trees by an interpretable generalization of their decision boundary. We develop a greedy method to efficiently construct CPT and scalable end-to-end training algorithms for the tree parameters when the tree structure is given.
  arXiv  Detail & Related papers  (2020-10-21T19:38:57Z)
• Deep Imitation Learning for Bimanual Robotic Manipulation [70.56142804957187]
  We present a deep imitation learning framework for robotic bimanual manipulation. A core challenge is to generalize the manipulation skills to objects in different locations. We propose to (i) decompose the multi-modal dynamics into elemental movement primitives, (ii) parameterize each primitive using a recurrent graph neural network to capture interactions, and (iii) integrate a high-level planner that composes primitives sequentially and a low-level controller to combine primitive dynamics and inverse kinematics control.
  arXiv  Detail & Related papers  (2020-10-11T01:40:03Z)
• A principled analysis of Behavior Trees and their generalisations [0.0]
  We analyse the principles behind Behavior Trees (BTs), an increasingly popular tree-structured control architecture. We show that reasoning via these principles leads to compatible solutions. We introduce a new class of control architectures we call generalised BTs or $k$-BTs.
  arXiv  Detail & Related papers  (2020-08-27T04:09:31Z)
• Tree-structured Attention with Hierarchical Accumulation [103.47584968330325]
  "Hierarchical Accumulation" encodes parse tree structures into self-attention at constant time complexity. Our approach outperforms SOTA methods in four IWSLT translation tasks and the WMT'14 English-German translation task.
  arXiv  Detail & Related papers  (2020-02-19T08:17:00Z)
• Parameterizing Branch-and-Bound Search Trees to Learn Branching Policies [76.83991682238666]
  Branch and Bound (B&B) is the exact tree search method typically used to solve Mixed-Integer Linear Programming problems (MILPs) We propose a novel imitation learning framework, and introduce new input features and architectures to represent branching.
  arXiv  Detail & Related papers  (2020-02-12T17:43:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.