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Found 6 papers, 4 papers with code
Working Backwards: Learning to Place by Picking
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).
Multimodal and Force-Matched Imitation Learning with a See-Through Visuotactile Sensor
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.
Learning from Guided Play: Improving Exploration for Adversarial Imitation Learning with Simple Auxiliary Tasks
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.
Learning Sequential Latent Variable Models from Multimodal Time Series Data
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.
Learning from Guided Play: A Scheduled Hierarchical Approach for Improving Exploration in Adversarial Imitation Learning
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.
Seeing All the Angles: Learning Multiview Manipulation Policies for Contact-Rich Tasks from Demonstrations
In this work, we demonstrate that a multiview policy can be found through imitation learning by collecting data from a variety of viewpoints.
