Learning Coordination Policies over Heterogeneous Graphs for Human-Robot Teams via Recurrent Neural Schedule Propagation

• URL: http://arxiv.org/abs/2301.13279v1
• Date: Mon, 30 Jan 2023 20:42:06 GMT
• Title: Learning Coordination Policies over Heterogeneous Graphs for Human-Robot Teams via Recurrent Neural Schedule Propagation
• Authors: Batuhan Altundas, Zheyuan Wang, Joshua Bishop and Matthew Gombolay
• Abstract summary: HybridNet is a deep learning-based framework for scheduling human-robot teams. We develop a virtual scheduling environment for mixed human-robot teams in a multiround setting.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As human-robot collaboration increases in the workforce, it becomes essential for human-robot teams to coordinate efficiently and intuitively. Traditional approaches for human-robot scheduling either utilize exact methods that are intractable for large-scale problems and struggle to account for stochastic, time varying human task performance, or application-specific heuristics that require expert domain knowledge to develop. We propose a deep learning-based framework, called HybridNet, combining a heterogeneous graph-based encoder with a recurrent schedule propagator for scheduling stochastic human-robot teams under upper- and lower-bound temporal constraints. The HybridNet's encoder leverages Heterogeneous Graph Attention Networks to model the initial environment and team dynamics while accounting for the constraints. By formulating task scheduling as a sequential decision-making process, the HybridNet's recurrent neural schedule propagator leverages Long Short-Term Memory (LSTM) models to propagate forward consequences of actions to carry out fast schedule generation, removing the need to interact with the environment between every task-agent pair selection. The resulting scheduling policy network provides a computationally lightweight yet highly expressive model that is end-to-end trainable via Reinforcement Learning algorithms. We develop a virtual task scheduling environment for mixed human-robot teams in a multi-round setting, capable of modeling the stochastic learning behaviors of human workers. Experimental results showed that HybridNet outperformed other human-robot scheduling solutions across problem sizes for both deterministic and stochastic human performance, with faster runtime compared to pure-GNN-based schedulers.

Related papers

• Robotic warehousing operations: a learn-then-optimize approach to large-scale neighborhood search [84.39855372157616]
  This paper supports robotic parts-to-picker operations in warehousing by optimizing order-workstation assignments, item-pod assignments and the schedule of order fulfillment at workstations. We solve it via large-scale neighborhood search, with a novel learn-then-optimize approach to subproblem generation. In collaboration with Amazon Robotics, we show that our model and algorithm generate much stronger solutions for practical problems than state-of-the-art approaches.
  arXiv  Detail & Related papers  (2024-08-29T20:22:22Z)
• Communication- and Computation-Efficient Distributed Decision-Making in Multi-Robot Networks [2.8936428431504164]
  We provide a distributed coordination paradigm that enables scalable and near-optimal joint motion planning among multiple robots. Our algorithm is up to two orders faster than competitive near-optimal algorithms. In simulations of surveillance tasks with up to 45 robots, it enables real-time planning at the order of 1 Hz with superior coverage performance.
  arXiv  Detail & Related papers  (2024-07-15T01:25:39Z)
• Dynamic Scheduling for Federated Edge Learning with Streaming Data [56.91063444859008]
  We consider a Federated Edge Learning (FEEL) system where training data are randomly generated over time at a set of distributed edge devices with long-term energy constraints. Due to limited communication resources and latency requirements, only a subset of devices is scheduled for participating in the local training process in every iteration.
  arXiv  Detail & Related papers  (2023-05-02T07:41:16Z)
• It Takes Two: Learning to Plan for Human-Robot Cooperative Carrying [0.6981715773998527]
  We present a method for predicting realistic motion plans for cooperative human-robot teams on a table-carrying task. We use a Variational Recurrent Neural Network, VRNN, to model the variation in the trajectory of a human-robot team over time. We show that the model generates more human-like motion compared to a baseline, centralized sampling-based planner.
  arXiv  Detail & Related papers  (2022-09-26T17:59:23Z)
• Model Predictive Control for Fluid Human-to-Robot Handovers [50.72520769938633]
  Planning motions that take human comfort into account is not a part of the human-robot handover process. We propose to generate smooth motions via an efficient model-predictive control framework. We conduct human-to-robot handover experiments on a diverse set of objects with several users.
  arXiv  Detail & Related papers  (2022-03-31T23:08:20Z)
• Semantic-Aware Collaborative Deep Reinforcement Learning Over Wireless Cellular Networks [82.02891936174221]
  Collaborative deep reinforcement learning (CDRL) algorithms in which multiple agents can coordinate over a wireless network is a promising approach. In this paper, a novel semantic-aware CDRL method is proposed to enable a group of untrained agents with semantically-linked DRL tasks to collaborate efficiently across a resource-constrained wireless cellular network.
  arXiv  Detail & Related papers  (2021-11-23T18:24:47Z)
• SABER: Data-Driven Motion Planner for Autonomously Navigating Heterogeneous Robots [112.2491765424719]
  We present an end-to-end online motion planning framework that uses a data-driven approach to navigate a heterogeneous robot team towards a global goal. We use model predictive control (SMPC) to calculate control inputs that satisfy robot dynamics, and consider uncertainty during obstacle avoidance with chance constraints. recurrent neural networks are used to provide a quick estimate of future state uncertainty considered in the SMPC finite-time horizon solution. A Deep Q-learning agent is employed to serve as a high-level path planner, providing the SMPC with target positions that move the robots towards a desired global goal.
  arXiv  Detail & Related papers  (2021-08-03T02:56:21Z)
• Rosella: A Self-Driving Distributed Scheduler for Heterogeneous Clusters [7.206919625027208]
  We present Rosella, a new self-driving, distributed approach for task scheduling in heterogeneous clusters. Rosella automatically learns the compute environment and adjusts its scheduling policy in real-time. We evaluate Rosella with a variety of workloads on a 32-node AWS cluster.
  arXiv  Detail & Related papers  (2020-10-28T20:12:29Z)
• Modeling Human Temporal Uncertainty in Human-Agent Teams [0.0]
  This paper builds a model of human timing uncertainty from a population of crowd-workers. We conclude that heavy-tailed distributions are the best models of human temporal uncertainty. We discuss how these results along with our collaborative online game will inform and facilitate future explorations into scheduling for improved human-robot fluency.
  arXiv  Detail & Related papers  (2020-10-09T23:43:59Z)
• Dynamic Multi-Robot Task Allocation under Uncertainty and Temporal Constraints [52.58352707495122]
  We present a multi-robot allocation algorithm that decouples the key computational challenges of sequential decision-making under uncertainty and multi-agent coordination. We validate our results over a wide range of simulations on two distinct domains: multi-arm conveyor belt pick-and-place and multi-drone delivery dispatch in a city.
  arXiv  Detail & Related papers  (2020-05-27T01:10:41Z)
• DeepSoCS: A Neural Scheduler for Heterogeneous System-on-Chip (SoC) Resource Scheduling [0.0]
  We present a novel scheduling solution for a class of System-on-Chip (SoC) systems. Our Deep Reinforcement Learning (DRL)-based Scheduler (DeepSoCS) overcomes the brittleness of rule-based schedulers.
  arXiv  Detail & Related papers  (2020-05-15T17:31:27Z)

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.