Who2com: Collaborative Perception via Learnable Handshake Communication
arXiv:2003.09575
Abstract
In this paper, we propose the problem of collaborative perception, where robots can combine their local observations with those of neighboring agents in a learnable way to improve accuracy on a perception task. Unlike existing work in robotics and multi-agent reinforcement learning, we formulate the problem as one where learned information must be shared across a set of agents in a bandwidth-sensitive manner to optimize for scene understanding tasks such as semantic segmentation. Inspired by networking communication protocols, we propose a multi-stage handshake communication mechanism where the neural network can learn to compress relevant information needed for each stage. Specifically, a target agent with degraded sensor data sends a compressed request, the other agents respond with matching scores, and the target agent determines who to connect with (i.e., receive information from). We additionally develop the AirSim-CP dataset and metrics based on the AirSim simulator where a group of aerial robots perceive diverse landscapes, such as roads, grasslands, buildings, etc. We show that for the semantic segmentation task, our handshake communication method significantly improves accuracy by approximately 20% over decentralized baselines, and is comparable to centralized ones using a quarter of the bandwidth.
Accepted to ICRA 2020
References in corpus (10)
- Rethinking Atrous Convolution for Semantic Image Segmentation
- Learning to Communicate with Deep Multi-Agent Reinforcement Learning
- Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
- Interaction Networks for Learning about Objects, Relations and Physics
- From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification
- Counterfactual Multi-Agent Policy Gradients
- Learning Attentional Communication for Multi-Agent Cooperation
- Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to Play StarCraft Combat Games
- Learning when to Communicate at Scale in Multiagent Cooperative and Competitive Tasks
- Graph Convolutional Reinforcement Learning