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cs.RO2025
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…
cs.RO2025
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…
cs.RO2024
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…