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This research introduces HATS, an MLLM-driven teleoperation framework that allows a single human to efficiently manage multi-arm robotic systems by delegating sub-tasks to an agent. By decoupling control between direct human manipulation and autonomous assistive arms, the system effectively mitigates cognitive load while maintaining high data collection quality for training complex policies. This advancement represents a significant step toward scalable robot learning in environments requiring coordinated movement beyond traditional dual-operator limits.
HATS: A Human-Agent Teleoperation System for Multi-Arm Data Collection
arXiv:2606.16491v1 Announce Type: new Abstract: Many real-world manipulation scenarios, such as handling complex collaborative tasks and dealing with large workspaces, require coordination of more than two robotic arms. Consequently, an effective multi-arm teleoperation system is required to collect demonstrations for training coordinated multi-arm manipulation policies. However, existing teleoperation frameworks mainly focus on single-operator or multi-operator setups, facing a practical trade-off between the cognitive load placed on a single operator and the coordination cost incurred by multiple operators. To address this problem, we introduce HATS, a human-agent teleoperation system that enables a single human operator, assisted by an MLLM-based agent, to collect data for multi-arm manipulation tasks. Our system decouples the control space: two primary arms are directly teleoperated by the human, while two assistive arms are controlled by a training-free agent that handles sub-tasks. In addition, the human operator can use voice commands to prevent collisions and correct assistive arm behaviors during execution. Extensive evaluations demonstrate that HATS achieves data collection efficiency and success rates comparable to expert dual-human teams. Moreover, downstream policy evaluations demonstrate the efficacy and quality of the data collected through HATS.
Abstract:Many real-world manipulation scenarios, such as handling complex collaborative tasks and dealing with large workspaces, require coordination of more than two robotic arms. Consequently, an effective multi-arm teleoperation system is required to collect demonstrations for training coordinated multi-arm manipulation policies. However, existing teleoperation frameworks mainly focus on single-operator or multi-operator setups, facing a practical trade-off between the cognitive load placed on a single operator and the coordination cost incurred by multiple operators. To address this problem, we introduce HATS, a human-agent teleoperation system that enables a single human operator, assisted by an MLLM-based agent, to collect data for multi-arm manipulation tasks. Our system decouples the control space: two primary arms are directly teleoperated by the human, while two assistive arms are controlled by a training-free agent that handles sub-tasks. In addition, the human operator can use voice commands to prevent collisions and correct assistive arm behaviors during execution. Extensive evaluations demonstrate that HATS achieves data collection efficiency and success rates comparable to expert dual-human teams. Moreover, downstream policy evaluations demonstrate the efficacy and quality of the data collected through HATS.
Robotics (cs.RO)
arXiv:2606.16491 [cs.RO]
(or arXiv:2606.16491v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.16491 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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