RGBD+DD-PPO

Reported on 4 benchmarks across 1 task · 1 paper · 4 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Robots4 results

Robot NavigationonHabitat 2020 Object Nav test-std
DISTANCE_TO_GOAL· 2019-11-01
9.31617
best: 10.52278 (Forward_Only)
SOTA
DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames arXiv:1911.00357
Robot NavigationonHabitat 2020 Object Nav test-std
SOFT_SPL· 2019-11-01
0.14718
best: 0.17955 (THDA)
SOTA
DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames arXiv:1911.00357
Robot NavigationonHabitat 2020 Object Nav test-std
SPL· 2019-11-01
0.02119
best: 0.08513 (THDA)
SOTA
DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames arXiv:1911.00357
Robot NavigationonHabitat 2020 Object Nav test-std
SUCCESS· 2019-11-01
0.06165
best: 0.21082 (6-Act Tether)
SOTA
DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames arXiv:1911.00357