arXiv:2606.06218v1 Announce Type: cross Abstract: A policy tuned for one robot often behaves differently on another, whether due to the sim-to-real gap, unknown payloads, or the differing dynamics of two instances of the same robot. In contact-rich, dynamic manipulation, even small motion discrepancies can result in failure to track reference motion, since they disrupt the timing and modes of contact. Common remedies, such as domain randomization or system identification, either produce overly conservative task policies or require data that must be recollected for each robot or payload. We int
The proliferation of advanced robotics, particularly in manipulation tasks, highlights the current limitations in robust motion transfer across different robot instances or environments.
Achieving more robust and adaptable robotic manipulation without extensive re-calibration or retraining can significantly accelerate the deployment and utility of robotics in diverse real-world settings.
This development proposes a method to make robotic manipulation policies more resilient to variations in robot dynamics and payloads, reducing the need for costly and time-consuming custom tuning.
- · Robotics manufacturers
- · Automation integrators
- · AI researchers in robotics
- · Logistics and manufacturing sectors
- · Companies relying on manual, repetitive manipulation tasks
- · Firms offering bespoke robot calibration services
Increased reliability and broader applicability of robotic systems in contact-rich manipulation tasks.
Faster and cheaper deployment of robotic solutions across various industries, driving down automation costs.
Accelerated development towards general-purpose robots capable of handling a wider range of unpredictable tasks with minimal human oversight.
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Read at arXiv cs.AI