← Search

Anirudh Nair

3 accepted papers

2023

Benchmarking Reinforcement Learning Techniques for Autonomous Navigation

ICRA 2023poster

Deep reinforcement learning (RL) has brought many successes for autonomous robot navigation. However, there still exists important limitations that prevent real-world use of RL-based navigation systems. For example, most learning approaches lack safety guarantees; and learned navigation systems may…

Cited by 51SourceScholar
2022

Socially CompliAnt Navigation Dataset (SCAND): A Large-Scale Dataset of Demonstrations for Social Navigation

RA-L 2022

Social navigation is the capability of an autonomous agent, such as a robot, to navigate in a “socially compliant” manner in the presence of other intelligent agents such as humans. With the emergence of autonomously navigating mobile robots in human-populated environments (e.g., domestic service ro

Cited by 195SourceScholar
2021

APPLR: Adaptive Planner Parameter Learning from Reinforcement

ICRA 2021poster

Classical navigation systems typically operate using a fixed set of hand-picked parameters (e.g. maximum speed, sampling rate, inflation radius, etc.) and require heavy expert re-tuning in order to work in new environments. To mitigate this requirement, it has been proposed to learn parameters for d…

Cited by 61SourceScholar