Semantic-Geometric Task Representations for Bimanual Manipulation from Human Demonstrations to Robot Action Planning
arXiv:2601.11460v2 Announce Type: replace-cross Abstract: Learning structured task representations from human demonstrations is essential for bimanual manipulation, where action ordering, object involvement, and interaction geometry vary significantly across executions. A key challenge lies in jointly capturing the discrete semantic task structure and the temporal evolution of object-centric geometric relations in a form that supports reasoning over task progression. We introduce a semantic--geometric graph-based task representation that jointly encodes object identities, inter-object semantic
This research provides a new method for complex robotic manipulation at a critical juncture for AI and robotics, driven by increasing computational power and advanced learning techniques.
Advanced bimanual manipulation is a key bottleneck for general-purpose robotics, impacting industries from manufacturing to healthcare by enabling more dexterous and autonomous systems.
The ability to jointly encode semantic and geometric information for complex tasks allows robots to learn and perform versatile operations more efficiently from human demonstrations.
- · Robotics companies
- · Automation sector
- · Logistics
- · Healthcare (surgical robotics)
- · Tasks requiring human dexterity
- · Laggard industrial robot manufacturers
Improved robot capabilities for complex assembly and handling tasks.
Reduced labor demand in sectors requiring high dexterity or precision.
Acceleration of humanoid robot development and widespread adoption in various industries.
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Read at arXiv cs.LG