WildLMa combines VR teleoperation with whole-body control, CLIP-based language-conditioned imitation learning, and an LLM planner to give a quadruped robot reusable manipulation skills that generalize to unseen objects and long-horizon tasks.
TeLeMan: Teleoperation for Legged Robot Loco-Manipulation using Wearable IMU-based Motion Capture
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abstract
Human life is invaluable. When dangerous or life-threatening tasks need to be completed, robotic platforms could be ideal in replacing human operators. Such a task that we focus on in this work is the Explosive Ordnance Disposal. Robot telepresence has the potential to provide safety solutions, given that mobile robots have shown robust capabilities when operating in several environments. However, autonomy may be challenging and risky at this stage, compared to human operation. Teleoperation could be a compromise between full robot autonomy and human presence. In this paper, we present a relatively cheap solution for telepresence and robot teleoperation, to assist with Explosive Ordnance Disposal, using a legged manipulator (i.e., a legged quadruped robot, embedded with a manipulator and RGB-D sensing). We propose a novel system integration for the non-trivial problem of quadruped manipulator whole-body control. Our system is based on a wearable IMU-based motion capture system that is used for teleoperation and a VR headset for visual telepresence. We experimentally validate our method in real-world, for loco-manipulation tasks that require whole-body robot control and visual telepresence.
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cs.RO 1years
2024 1verdicts
CONDITIONAL 1representative citing papers
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WildLMa: Long Horizon Loco-Manipulation in the Wild
WildLMa combines VR teleoperation with whole-body control, CLIP-based language-conditioned imitation learning, and an LLM planner to give a quadruped robot reusable manipulation skills that generalize to unseen objects and long-horizon tasks.