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LimX Dynamics

Industry researchasia · cn
Research library5linked papers
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Selected work

Representative Papers

Spatiotemporal Agility: Time-Constrained Reinforcement Learning for Vision-Guided Dynamic Quadrupedal Interception

Aug 07, 2026

This work addresses the challenge of enabling quadrupedal robots to accurately intercept fast-moving dynamic targets under stringent time constraints, a task hindered by insufficient spatiotemporal precision and perception-to-control latency in existing velocity-tracking approaches. The authors propose an end-to-end dynamic interception framework that directly conditions the reinforcement learning policy on visually predicted target landing position and time of arrival, bypassing intermediate velocity commands to avoid error accumulation. Integrating multi-camera perception, online trajectory prediction, low-latency communication, and sim-to-real locomotion control, the system achieves the first closed-loop dynamic interception on a quadruped. Evaluated on toss-interception tasks within 2 meters and flight durations of 0.8–1.2 seconds, the method significantly outperforms velocity-tracking baselines in success rate and exhibits less performance degradation after real-world deployment, demonstrating its efficacy and robustness.

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Any2Any: Efficient Cross-Embodiment Transfer for Humanoid Whole-Body Tracking

May 22, 2026

This work addresses the high cost and slow deployment associated with training humanoid whole-body tracking models from scratch. The authors propose the Any2Any transfer paradigm, which leverages kinematic alignment and lightweight dynamic adaptation, combined with parameter-efficient fine-tuning (PEFT) and reuse of pretraining strategies, to effectively transfer high-performance tracking capabilities to new robot morphologies. Requiring only 1% of the original data and computational resources, this approach achieves convergence significantly faster across multiple robotic platforms while delivering performance comparable to or even surpassing that of models trained from scratch. By drastically reducing the resource overhead, the method substantially lowers the barrier to deploying advanced tracking systems on diverse humanoid robots.

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Recent publications

Latest Papers

Spatiotemporal Agility: Time-Constrained Reinforcement Learning for Vision-Guided Dynamic Quadrupedal Interception

Aug 07, 2026

This work addresses the challenge of enabling quadrupedal robots to accurately intercept fast-moving dynamic targets under stringent time constraints, a task hindered by insufficient spatiotemporal precision and perception-to-control latency in existing velocity-tracking approaches. The authors propose an end-to-end dynamic interception framework that directly conditions the reinforcement learning policy on visually predicted target landing position and time of arrival, bypassing intermediate velocity commands to avoid error accumulation. Integrating multi-camera perception, online trajectory prediction, low-latency communication, and sim-to-real locomotion control, the system achieves the first closed-loop dynamic interception on a quadruped. Evaluated on toss-interception tasks within 2 meters and flight durations of 0.8–1.2 seconds, the method significantly outperforms velocity-tracking baselines in success rate and exhibits less performance degradation after real-world deployment, demonstrating its efficacy and robustness.

0 citationsRead paper

Any2Any: Efficient Cross-Embodiment Transfer for Humanoid Whole-Body Tracking

May 22, 2026

This work addresses the high cost and slow deployment associated with training humanoid whole-body tracking models from scratch. The authors propose the Any2Any transfer paradigm, which leverages kinematic alignment and lightweight dynamic adaptation, combined with parameter-efficient fine-tuning (PEFT) and reuse of pretraining strategies, to effectively transfer high-performance tracking capabilities to new robot morphologies. Requiring only 1% of the original data and computational resources, this approach achieves convergence significantly faster across multiple robotic platforms while delivering performance comparable to or even surpassing that of models trained from scratch. By drastically reducing the resource overhead, the method substantially lowers the barrier to deploying advanced tracking systems on diverse humanoid robots.

0 citationsRead paper