18 citations · 74 across the 11 of their papers we have counts for
11 papers
Learning Cross-Spectral Point Features with Task-Oriented Training
Mia Thomas, Trevor Ablett, Jonathan Kelly
Unmanned aerial vehicles (UAVs) enable operations in remote and hazardous environments, yet the visible-spectrum, camera-based navigation systems often relied upon by UAVs struggle…
Efficient Imitation Without Demonstrations via Value-Penalized Auxiliary Control from Examples
Trevor Ablett, Bryan Chan, Jayce Haoran Wang +1
Common approaches to providing feedback in reinforcement learning are the use of hand-crafted rewards or full-trajectory expert demonstrations. Alternatively, one can use examples…
Working Backwards: Learning to Place by Picking
Oliver Limoyo, Abhisek Konar, Trevor Ablett +3
We present placing via picking (PvP), a method to autonomously collect real-world demonstrations for a family of placing tasks in which objects must be manipulated to specific, con…
Multimodal and Force-Matched Imitation Learning with a See-Through Visuotactile Sensor
Trevor Ablett, Oliver Limoyo, Adam Sigal +5
Contact-rich tasks continue to present many challenges for robotic manipulation. In this work, we leverage a multimodal visuotactile sensor within the framework of imitation learni…
Learning from Guided Play: Improving Exploration for Adversarial Imitation Learning with Simple Auxiliary Tasks
Trevor Ablett, Bryan Chan, Jonathan Kelly
Adversarial imitation learning (AIL) has become a popular alternative to supervised imitation learning that reduces the distribution shift suffered by the latter. However, AIL requ…
Learning Sequential Latent Variable Models from Multimodal Time Series Data
Oliver Limoyo, Trevor Ablett, Jonathan Kelly
Sequential modelling of high-dimensional data is an important problem that appears in many domains including model-based reinforcement learning and dynamics identification for cont…