Daniel Lenton

I'm a PhD student in The Dyson Robotics Lab at Imperial College London. In my research, I'm exploring the intersection between learning-based geometric reresentations, ego-centric perception, spatial memory, and visuomotor control for robotics.

I am also:

Creator of Ivy, where we're on a mission to unify all Machine Learning (ML) frameworks, join us on our mission and lets-unify.ai!

Co-organizer of The RoundTable Chatroom, where we host events at top AI conferences every year.

Teaching assistant at Imperial College London, having taught courses on robot navigation, algorithms, intro to AI, deep learning and software engineering design.

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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 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.

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, 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 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.