Search Results for author:
Found 10 papers, 3 papers with code
HEIGHT: Heterogeneous Interaction Graph Transformer for Robot Navigation in Crowded and Constrained Environments
Based on the heterogeneous st-graph, we propose HEIGHT, a novel navigation policy network architecture with different components to capture heterogeneous interactions among entities through space and time.
Towards Real-Time Generation of Delay-Compensated Video Feeds for Outdoor Mobile Robot Teleoperation
Teleoperation is an important technology to enable supervisors to control agricultural robots remotely.
Cooperative Advisory Residual Policies for Congestion Mitigation
Our policies are trained in simulation with our novel instruction adherence driver model, and evaluated in simulation and through a user study (N=16) to capture the sentiments of human drivers.
Structured Graph Network for Constrained Robot Crowd Navigation with Low Fidelity Simulation
We investigate the feasibility of deploying reinforcement learning (RL) policies for constrained crowd navigation using a low-fidelity simulator.
Hallucination Detection in Foundation Models for Decision-Making: A Flexible Definition and Review of the State of the Art
The rise of foundation models trained on multiple tasks with impressively large datasets from a variety of fields has led researchers to believe that these models may provide common sense reasoning that existing planners are missing.
PeRP: Personalized Residual Policies For Congestion Mitigation Through Co-operative Advisory Systems
To this end, we develop a co-operative advisory system based on PC policies with a novel driver trait conditioned Personalized Residual Policy, PeRP.
Towards Co-operative Congestion Mitigation
The effects of traffic congestion are widespread and are an impedance to everyday life.
Intention Aware Robot Crowd Navigation with Attention-Based Interaction Graph
We study the problem of safe and intention-aware robot navigation in dense and interactive crowds.
Learning to Navigate Intersections with Unsupervised Driver Trait Inference
Then, we use this trait representation to learn a policy for an autonomous vehicle to navigate through a T-intersection with deep reinforcement learning.
Decentralized Structural-RNN for Robot Crowd Navigation with Deep Reinforcement Learning
Safe and efficient navigation through human crowds is an essential capability for mobile robots.
