TL;DR
BiCoord introduces a comprehensive benchmark for long-horizon, tightly coordinated bimanual manipulation tasks, addressing the limitations of existing short-horizon, loosely coordinated benchmarks.
Contribution
It presents a new benchmark with diverse tasks, quantitative metrics, and baseline evaluations to advance research in long-term, coordinated bimanual robotic manipulation.
Findings
Existing policies struggle with long-duration, highly coupled tasks.
BiCoord's metrics enable systematic evaluation of coordination.
The benchmark reveals fundamental challenges in achieving tight bimanual coordination.
Abstract
Bimanual manipulation, i.e., the coordinated use of two robotic arms to complete tasks, is essential for achieving human-level dexterity in robotics. Recent simulation benchmarks, e.g., RoboTwin and RLBench2, have advanced data-driven learning for bimanual manipulation. However, existing tasks are short-horizon and only loosely coordinated, failing to capture the spatial-temporal coupling inherent in real-world bimanual behaviors. To address this gap, we introduce BiCoord, a benchmark for long-horizon and tightly coordinated bimanual manipulation. Specifically, BiCoord comprises diverse tasks that require continuous inter-arm dependency and dynamic role exchange across multiple sub-goals. Also, we propose a suite of quantitative metrics that evaluate coordination from temporal, spatial, and spatial-temporal perspectives, enabling systematic measurement of bimanual cooperation.…
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