Embodied intelligent algorithm

<p>Embodied intelligent algorithm: (VLA model, imitation learning, multimodal basic model)</p><p>Duties:</p><p>1. Responsible for developing a multimodal decision model for a two-arm robot to complete complex physical tasks in a real environment, and pushing the VLA (visual-language-action) foundation model of robots to the ground;</p><p>2. Responsible for data collection design, algorithm architecture design, model training, engineering deployment and continuous performance optimization, deep collaboration with hardware, data platform, application team to build end-to-end system-level solutions;</p><p>3. Continuously explore innovative applications of multimodal large models (such as VLM, VLA, VLN, etc.) in the embodied intelligence field, and promote algorithmic technology transformation from laboratory to actual scenes;</p><p>4. Follow industry and academic frontiers, reproduce, optimize and transform the latest embodied intelligence-related technical achievements. </p><p>Requirements:</p><p>1. Master's degree or above, majoring in Computer Science, Artificial Intelligence, Robotics, Automation, etc., solidly mastering core algorithms such as robot learning, deep learning, and imitation learning;</p><p>2. Proficient in Python, with good C++/Python programming habits, and familiar with mainstream deep learning frameworks such as PyTorch/TensorFlow;</p><p>3. Have multi-modal large models (VLM/VLA, etc.) in robot perception, operation, navigation, etc. Research and landing experience, or have in-depth research background on related algorithms;</p><p>4. Familiar with visual, language, etc. multi-modal fusion algorithm, with multi-modal large model tuning experience, and understanding of embodied intelligence field frontier innovation progress;</p><p>5. Have good self-motivation, stress resistance, and team collaboration, cross-department communication skills, and be passionate about robots' embodied intelligence.</p><p></p><p>1. Have published influential work in professional conference journals in related fields such as robotics, machine learning, computer vision (TRO, RSS, ICRA, CoRL , NeurIPS, ICLR, CVPR, etc.), or have professional academic competition winning experience;</p><p>2. Have large-scale data collection-algorithm co-ordination (data flywheel) system development experience;</p><p>3. Familiar with mainstream robot simulation platforms such as IsaacGym/Sim, SAPIEN, MuJoCo, Gazebo; 4. Have relevant experience in the multi-modal large model field or core contributions to open source projects;</p><p>5. Familiar with reinforcement learning, robot control algorithms, multimodal perception, etc. related algorithms.</p>