no code implementations • 4 Dec 2023 • Oliver Limoyo, Abhisek Konar, Trevor Ablett, Jonathan Kelly, Francois R. Hogan, Gregory Dudek
By doing so, the policy can generalize to object placement scenarios outside of the training environment without privileged information (e. g., placing a plate picked up from a table).
no code implementations • 2 Nov 2023 • Trevor Ablett, Oliver Limoyo, Adam Sigal, Affan Jilani, Jonathan Kelly, Kaleem Siddiqi, Francois Hogan, Gregory Dudek
An STS sensor can be switched between visual and tactile modes by leveraging a semi-transparent surface and controllable lighting, allowing for both pre-contact visual sensing and during-contact tactile sensing with a single sensor.
1 code implementation • 30 Dec 2022 • Trevor Ablett, Bryan Chan, Jonathan Kelly
In this work, we show that the standard, naive approach to exploration can manifest as a suboptimal local maximum if a policy learned with AIL sufficiently matches the expert distribution without fully learning the desired task.
1 code implementation • 21 Apr 2022 • Oliver Limoyo, Trevor Ablett, Jonathan Kelly
In this work, we present a self-supervised generative modelling framework to jointly learn a probabilistic latent state representation of multimodal data and the respective dynamics.
1 code implementation • 16 Dec 2021 • Trevor Ablett, Bryan Chan, Jonathan Kelly
We present Learning from Guided Play (LfGP), a framework in which we leverage expert demonstrations of, in addition to a main task, multiple auxiliary tasks.
1 code implementation • 28 Apr 2021 • Trevor Ablett, Yifan Zhai, Jonathan Kelly
In this work, we demonstrate that a multiview policy can be found through imitation learning by collecting data from a variety of viewpoints.