Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
End-to-End Egospheric Spatial Memory
Authors: Daniel James Lenton, Stephen James, Ronald Clark, Andrew Davison
ICLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through our broad set of experiments, we show that ESM provides a general computation graph for embodied spatial reasoning, and the module forms a bridge between real-time mapping systems and differentiable memory architectures. |
| Researcher Affiliation | Collaboration | Daniel Lenton 1, Stephen James 1, Ronald Clark 2, Andrew J. Davison 1 1 Dyson Robotics Lab, 2 Department of Computing, Imperial College London |
| Pseudocode | Yes | Algorithm 1: ESM Step |
| Open Source Code | Yes | Implementation at: https://github.com/ivy-dl/memory. |
| Open Datasets | Yes | For the object segmentation experiment, we use downsampled 60 80 and 120 160 images from the Scan Net dataset, which we ο¬rst RGB-Depth align. |
| Dataset Splits | Yes | The losses for each network evaluated on the training set and validation set during the course of training are presented in Fig 12. |
| Hardware Specification | Yes | running on Nvidia RTX 2080 GPU |
| Software Dependencies | Yes | The implementation of our module is therefore jointly compatible with Tensor Flow 2.0, Py Torch, MXNet, Jax and Numpy. |
| Experiment Setup | Yes | All networks use a batch size of 16, an unroll size of 16, and are trained for 250k steps using an ADAM optimizer with 1e 4 learning rate. |