Action aided Joint Embedding Prediction on 2D LiDAR Sequences for Robust Mobile Robot Navigation in Dynamic Environments
Abstract
Mobile robots that navigate among moving obstacles must act on 2D LiDAR
scans that are sparse, noisy, and change from one step to the next.
Reinforcement learning policies such as PPO are usually trained on these
raw scans directly. A single scan, however, does not show how obstacles
are moving, nor how much of the change between consecutive scans comes
from the robot's own motion. The policy has to learn both perception and
control from a sparse reward, and in our baseline experiments this made
performance sensitive to sensor noise and highly variable across random
seeds. Supervising a better representation would require labels for
obstacle motion, which are costly to collect outside simulation. We
therefore ask whether a self-supervised model, trained only on unlabelled
LiDAR sequences and the robot's actions, can learn a representation of
obstacle dynamics that improves a downstream PPO policy's success rate
under sensor noise and reduces its variability across seeds.
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BibTeX
@misc{narashiman2026action,
title = {{Action aided Joint Embedding Prediction on 2D LiDAR Sequences for Robust Mobile Robot Navigation in Dynamic Environments}},
author = {Tarun Narashiman},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.5281/zenodo.23540677},
url = {https://zenodo.org/records/23540677},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Action aided Joint Embedding Prediction on 2D LiDAR Sequences for Robust Mobile Robot Navigation in Dynamic Environments
AU - Tarun Narashiman
PY - 2026
DA - 2026/10/21
PB - Zenodo
DO - 10.5281/zenodo.23540677
UR - https://zenodo.org/records/23540677
AB - Mobile robots that navigate among moving obstacles must act on 2D LiDAR scans that are sparse, noisy, and change from one step to the next. Reinforcement learning policies such as PPO are usually trained on these raw scans directly. A single scan, however, does not show how obstacles are moving, nor how much of the change between consecutive scans comes from the robot's own motion. The policy has to learn both perception and control from a sparse reward, and in our baseline experiments this made performance sensitive to sensor noise and highly variable across random seeds. Supervising a better representation would require labels for obstacle motion, which are costly to collect outside simulation. We therefore ask whether a self-supervised model, trained only on unlabelled LiDAR sequences and the robot's actions, can learn a representation of obstacle dynamics that improves a downstream PPO policy's success rate under sensor noise and reduces its variability across seeds.
ER -