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A low-cost and lightweight 6 DoF bimanual arm for dynamic and contact-rich manipulation

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abstract

Dynamic and contact-rich object manipulation, such as striking, snatching, or hammering, remains challenging for robotic systems due to hardware limitations. Most existing robots are constrained by high-inertia design, limited compliance, and reliance on expensive torque sensors. To address this, we introduce ARMADA (Affordable Robot for Manipulation and Dynamic Actions), a 6 degrees-of-freedom bimanual robot designed for dynamic manipulation research. ARMADA combines low-inertia, back-drivable actuators with a lightweight design, using readily available components and 3D-printed links for ease of assembly in research labs. The entire system, including both arms, is built for just $6,100. Each arm achieves speeds up to 6.16m/s, almost twice that of most collaborative robots, with a comparable payload of 2.5kg. We demonstrate ARMADA can perform dynamic manipulation like snatching, hammering, and bimanual throwing in real-world environments. We also showcase its effectiveness in reinforcement learning (RL) by training a non-prehensile manipulation policy in simulation and transferring it zero-shot to the real world, as well as human motion shadowing for dynamic bimanual object throwing. ARMADA is fully open-sourced with detailed assembly instructions, CAD models, URDFs, simulation, and learning codes. We highly recommend viewing the supplementary video at https://sites.google.com/view/im2-humanoid-arm.

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cs.RO 1

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2025 1

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representative citing papers

NeuralSVCD for Efficient Swept Volume Collision Detection

cs.RO · 2025-08-30 · conditional · novelty 6.0

A neural encoder-decoder with sphere-based broad-phase filtering performs swept-volume collision detection continuously along trajectories, beating baselines in accuracy and speed on manipulation benchmarks.

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  • NeuralSVCD for Efficient Swept Volume Collision Detection cs.RO · 2025-08-30 · conditional · none · ref 25 · internal anchor

    A neural encoder-decoder with sphere-based broad-phase filtering performs swept-volume collision detection continuously along trajectories, beating baselines in accuracy and speed on manipulation benchmarks.