130 citations · 297 across the 16 of their papers we have counts for
7 papers · 1 filter
Scaling Instructable Agents Across Many Simulated Worlds
SIMA Team, Maria Abi Raad, Arun Ahuja +91
Building embodied AI systems that can follow arbitrary language instructions in any 3D environment is a key challenge for creating general AI. Accomplishing this goal requires lear…
Reaching Through Latent Space: From Joint Statistics to Path Planning in Manipulation
Chia-Man Hung, Shaohong Zhong, Walter Goodwin +4
We present a novel approach to path planning for robotic manipulators, in which paths are produced via iterative optimisation in the latent space of a generative model of robot pos…
VAE-Loco: Versatile Quadruped Locomotion by Learning a Disentangled Gait Representation
Alexander L. Mitchell, Wolfgang Merkt, Mathieu Geisert +5
Quadruped locomotion is rapidly maturing to a degree where robots are able to realise highly dynamic manoeuvres. However, current planners are unable to vary key gait parameters of…
Next Steps: Learning a Disentangled Gait Representation for Versatile Quadruped Locomotion
Alexander L. Mitchell, Wolfgang Merkt, Mathieu Geisert +5
Quadruped locomotion is rapidly maturing to a degree where robots now routinely traverse a variety of unstructured terrains. However, while gaits can be varied typically by selecti…
APEX: Unsupervised, Object-Centric Scene Segmentation and Tracking for Robot Manipulation
Yizhe Wu, Oiwi Parker Jones, Martin Engelcke +1
Recent advances in unsupervised learning for object detection, segmentation, and tracking hold significant promise for applications in robotics. A common approach is to frame these…
First Steps: Latent-Space Control with Semantic Constraints for Quadruped Locomotion
Alexander L. Mitchell, Martin Engelcke, Oiwi Parker Jones +5
Traditional approaches to quadruped control frequently employ simplified, hand-derived models. This significantly reduces the capability of the robot since its effective kinematic…
Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks
Martin Engelcke, Dushyant Rao, Dominic Zeng Wang +2
This paper proposes a computationally efficient approach to detecting objects natively in 3D point clouds using convolutional neural networks (CNNs). In particular, this is achieve…