Robotics: Science and Systems XXII
SimToolReal: An Object-Centric Policy for Zero-Shot Dexterous Tool Manipulation
Kushal Kedia, Tyler Ga Wei Lum, Jeannette Bohg, Karen LiuAbstract:
The ability to manipulate tools significantly expands the set of tasks a robot can perform. Yet, tool manipulation represents a challenging class of dexterity, requiring grasping thin objects, in-hand object rotations, and forceful interactions. Since collecting teleoperation data for these behaviors is challenging, sim-to-real reinforcement learning (RL) is a promising alternative. However, prior approaches typically require substantial engineering effort to model objects and tune reward functions for each task. In this work, we propose SimToolReal, taking a step towards generalizing sim-to-real RL policies for tool manipulation. Instead of focusing on a single object and task, we procedurally generate a large variety of tool-like object primitives in simulation and train a single RL policy with the universal goal of manipulating each object to random goal poses. This approach enables SimToolReal to perform general dexterous tool manipulation at test-time without any object or task-specific training. We demonstrate that SimToolReal outperforms prior retargeting and fixed-grasp methods by 37% while matching the performance of specialist RL policies trained on specific target objects and tasks. Finally, we show that SimToolReal generalizes across a diverse set of everyday tools, achieving strong zero-shot performance over 120 real-world rollouts spanning 24 tasks, 12 object instances, and 6 tool categories.
Bibtex:
@INPROCEEDINGS{KediaK-RSS-26,
AUTHOR = {Kushal Kedia AND Tyler Ga Wei Lum AND Jeannette Bohg AND Karen Liu},
TITLE = {{SimToolReal: An Object-Centric Policy for Zero-Shot Dexterous Tool Manipulation}},
BOOKTITLE = {Proceedings of Robotics: Science and Systems},
YEAR = {2026},
ADDRESS = {Sydney, Australia},
MONTH = {July},
DOI = {10.15607/RSS.2026.XXII.151}
}
