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Found 10 papers, 7 papers with code
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.
DRAGON: A Dialogue-Based Robot for Assistive Navigation with Visual Language Grounding
Motivated by recent advances in visual-language grounding and semantic navigation, we propose DRAGON, a guiding robot powered by a dialogue system and the ability to associate the environment with natural language.
Occlusion-Aware Crowd Navigation Using People as Sensors
To the best of our knowledge, this work is the first to use social occlusion inference for crowd navigation.
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.
Off Environment Evaluation Using Convex Risk Minimization
We also show that the our method is able to estimate performance of a 7 DOF robotic arm using the simulator and remotely collected data from the robot in the real world.
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.
Learning Visual-Audio Representations for Voice-Controlled Robots
Previous methods rely on a large number of labels and task-specific reward functions.
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.
Robot Sound Interpretation: Combining Sight and Sound in Learning-Based Control
We explore the interpretation of sound for robot decision making, inspired by human speech comprehension.
Robust Deep Reinforcement Learning with Adversarial Attacks
This paper proposes adversarial attacks for Reinforcement Learning (RL) and then improves the robustness of Deep Reinforcement Learning algorithms (DRL) to parameter uncertainties with the help of these attacks.
