Zero-Shot Transfer of Haptics-Based Object Insertion Policies

• URL: http://arxiv.org/abs/2301.12587v3
• Date: Thu, 8 Jun 2023 01:16:00 GMT
• Title: Zero-Shot Transfer of Haptics-Based Object Insertion Policies
• Authors: Samarth Brahmbhatt, Ankur Deka, Andrew Spielberg, Matthias M\"uller
• Abstract summary: Humans naturally exploit haptic feedback during contact-rich tasks like loading a dishwasher or stocking a bookshelf. Current robotic systems focus on avoiding unexpected contact, often relying on strategically placed environment sensors. We train a contact-exploiting manipulation policy in simulation for the contact-rich household task of loading plates into a slotted holder.
• Score: 11.711534127073492
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Humans naturally exploit haptic feedback during contact-rich tasks like loading a dishwasher or stocking a bookshelf. Current robotic systems focus on avoiding unexpected contact, often relying on strategically placed environment sensors. Recently, contact-exploiting manipulation policies have been trained in simulation and deployed on real robots. However, they require some form of real-world adaptation to bridge the sim-to-real gap, which might not be feasible in all scenarios. In this paper we train a contact-exploiting manipulation policy in simulation for the contact-rich household task of loading plates into a slotted holder, which transfers without any fine-tuning to the real robot. We investigate various factors necessary for this zero-shot transfer, like time delay modeling, memory representation, and domain randomization. Our policy transfers with minimal sim-to-real gap and significantly outperforms heuristic and learnt baselines. It also generalizes to plates of different sizes and weights. Demonstration videos and code are available at https://sites.google.com/view/compliant-object-insertion.

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