Daniel Lenton

Creator of Ivy, where we're on a mission to unify all AI frameworks, infrastructure and hardware. Learn more at unify.ai

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Research and Publications
End-to-End Egospheric Spatial Memory
Daniel lenton, Stephen James, Ronald Clark, Andrew Davison
International Conference on Learning Representations (ICLR), 2021
paper / video / code / project page

ESM encodes the memory in an ego-sphere around the agent, enabling expressive 3D representations. ESM can be trained end-to-end via either imitation or reinforcement learning, and improves both training efficiency and final performance against other memory baselines on visuomotor control tasks.

Ivy: Templated Deep Learning for Inter-Framework Portability
Daniel lenton, Fabio Pardo, Fabian Falck, Stephen James, Ronald Clark
arXiv, 2021
paper / video / code / project page

Ivy is a templated Machine Learning (ML) framework which abstracts existing ML frameworks such that their core functions all exhibit consistent call signatures and syntax. Ivy allows high-level framework-agnostic functions, layers and libraries to be implemented on top.

Unsupervised Path Regression Networks
Michal Pandy, Daniel lenton, Ronald Clark
International Conference on Intelligent Robots and Systems (IROS), 2021
paper / project page

We demonstrate that challenging shortest path problems can be solved via direct spline regression from a neural network, trained in an unsupervised manner without requiring ground truth optimal paths for training. To achieve this, we derive a geometry-dependent optimal cost function whose minima guarantees collision-free solutions.

Waypoint Planning Networks
Alexandru-Iosif Toma, Hussein Ali Jaafar, Hao-Ya Hsueh, Stephen James, Daniel lenton, Ronald Clark, Sajad Saeedi,
International Conference on Computer Vision and Robotics (CVR), 2021
paper / video / code / project page /

Waypoint Planning Networks, or WPN, is a hybrid motion planning algorithm based on LSTMs with a local kernel, a classic algorithm such as A*, and a global kernel using a learned algorithm. WPN produces a more computationally efficient and robust solution than other learned approaches.

MoreFusion: Multi-object Reasoning for 6D Pose Estimation from Volumetric Fusion
Kentaro Wada, Edgar Sucar, Stephen James, Daniel lenton, Andrew Davison
Conference on Computer Vision and Pattern Recognition (CVPR), 2020
paper / video / code / project page

MoreFusion makes 3D object pose proposals from single RGB-D views, accumulates pose estimates and non-parametric occupancy information from multiple views as the camera moves, and performs joint optimization to estimate consistent, non-intersecting poses for multiple objects in contact.