1.6k citations · 1.7k across the 17 of their papers we have counts for
5 papers · 1 filter
Benchmarking Actor-Critic Deep Reinforcement Learning Algorithms for Robotics Control with Action Constraints
Kazumi Kasaura, Shuwa Miura, Tadashi Kozuno +3
This study presents a benchmark for evaluating action-constrained reinforcement learning (RL) algorithms. In action-constrained RL, each action taken by the learning system must co…
Counterfactual Fairness Filter for Fair-Delay Multi-Robot Navigation
Hikaru Asano, Ryo Yonetani, Mai Nishimura +1
Multi-robot navigation is the task of finding trajectories for a team of robotic agents to reach their destinations as quickly as possible without collisions. In this work, we intr…
When to Replan? An Adaptive Replanning Strategy for Autonomous Navigation using Deep Reinforcement Learning
Kohei Honda, Ryo Yonetani, Mai Nishimura +1
The hierarchy of global and local planners is one of the most commonly utilized system designs in autonomous robot navigation. While the global planner generates a reference path f…
Risk-aware Path Planning via Probabilistic Fusion of Traversability Prediction for Planetary Rovers on Heterogeneous Terrains
Masafumi Endo, Tatsunori Taniai, Ryo Yonetani +1
Machine learning (ML) plays a crucial role in assessing traversability for autonomous rover operations on deformable terrains but suffers from inevitable prediction errors. Especia…
Periodic Multi-Agent Path Planning
Kazumi Kasaura, Ryo Yonetani, Mai Nishimura
Multi-agent path planning (MAPP) is the problem of planning collision-free trajectories from start to goal locations for a team of agents. This work explores a relatively unexplore…