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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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.