aiqbench analysis
This research addresses the sim-to-real gap in robotic manipulation by using pseudo-tactile feedback derived from force-controlled grippers to resolve state ambiguity. The approach enables robust imitation learning policies through pure simulation training, eliminating the need for additional hardware or extensive real-world data collection. By providing noise-free binary observations, this method significantly enhances policy efficiency across various grasp-based tasks without altering the underlying robotic infrastructure.
Disambiguate Gripper State in Grasp-Based Tasks: Pseudo-Tactile as Feedback Enables Pure Simulation Learning
arXiv:2503.23835v2 Announce Type: replace Abstract: Grasp-based manipulation tasks are fundamental to robots interacting with their environments, yet gripper state ambiguity significantly reduces the robustness of imitation learning policies for these tasks. Data-driven solutions face the challenge of high real-world data costs, while simulation data, despite its low costs, is limited by the sim-to-real gap. We identify the root cause of gripper state ambiguity as the lack of tactile feedback. To address this, we propose a novel approach employing pseudo-tactile as feedback, inspired by the idea of using a force-controlled gripper as a tactile sensor. This method enhances policy robustness without additional data collection and hardware involvement, while providing a noise-free binary gripper state observation for the policy and thus facilitating pure simulation learning to unleash the power of simulation. Experimental results across three real-world grasp-based tasks demonstrate the necessity, effectiveness, and efficiency of our approach.
Abstract:Grasp-based manipulation tasks are fundamental to robots interacting with their environments, yet gripper state ambiguity significantly reduces the robustness of imitation learning policies for these tasks. Data-driven solutions face the challenge of high real-world data costs, while simulation data, despite its low costs, is limited by the sim-to-real gap. We identify the root cause of gripper state ambiguity as the lack of tactile feedback. To address this, we propose a novel approach employing pseudo-tactile as feedback, inspired by the idea of using a force-controlled gripper as a tactile sensor. This method enhances policy robustness without additional data collection and hardware involvement, while providing a noise-free binary gripper state observation for the policy and thus facilitating pure simulation learning to unleash the power of simulation. Experimental results across three real-world grasp-based tasks demonstrate the necessity, effectiveness, and efficiency of our approach.
Robotics (cs.RO)
arXiv:2503.23835 [cs.RO]
(or arXiv:2503.23835v2 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2503.23835 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 231 words
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