OmniTacTune: Policy-Agnostic Real-World RL for Tactile Residual Adaptation of Visual Policies
arXiv:2607.03723v1 Announce Type: cross Abstract: Visual policies learned from human videos, teleoperation, and robot demonstrations offer scalable motion priors, but often fail in contact-rich manipulation, where success significantly depends on local force and contact geometry. Tactile sensing provides these complementary signals, yet tactile data remain costly to collect and hard to generalize across sensors, robots, and tasks. We introduce OmniTacTune, a policy-agnostic real-world RL pipeline that adapts tactile feedback to pretrained visual policies through residual correction. OmniTacTun
The increased maturity of visual policies and the persistent limitations in contact-rich manipulation emphasize the need for effective tactile integration, which OmniTacTune addresses through real-world reinforcement learning.
This development represents a significant step towards enabling robots to perform highly dexterous and reliable physical interactions, critical for complex manipulation tasks in various industries.
Robotics can now more efficiently integrate tactile feedback to refine existing visual policies, allowing for more robust and adaptive manipulation without extensive tactile data collection.
- · Robotics industry
- · Automation companies
- · Manufacturing sector
- · Manual labor in precision tasks
Robots will achieve higher success rates in tasks requiring fine motor control and physical interaction.
This improved dexterity will accelerate the deployment of robots into new and more complex application areas.
Increased robot capabilities could lead to new types of human-robot collaboration and service models in everyday environments.
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Read at arXiv cs.AI