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Seeking new worlds, new marvels - with you.
We are recruiting highly motivated PhD students and Research Assistants to join our team in Embodied Intelligence and Robot Learning. Students will work on cutting-edge research in robot world models, vision-language-action models, learning from failures, self-correction, and real-world robot deployment.
We welcome applicants with backgrounds in robotics, machine learning, computer vision, reinforcement learning, or related areas who are interested in building intelligent robots that can imagine, reason, learn, and act in the physical world.
We are seeking highly motivated PhD students and Research Assistants to join our team in Embodied Intelligence and Robot Learning. Our goal is to build robots that can imagine future outcomes, learn from failures, self-correct, and perform complex tasks in the real world.
Robot World Models
Learning action-conditioned models for future prediction, planning, and policy evaluation.
Vision-Language-Action Models
Developing multimodal policies that combine perception, reasoning, visual subgoals, and action generation.
Learning from Failures
Enabling robots to identify mistakes, recover from failures, and improve with limited demonstrations.
Real-World Robot Deployment
Building reliable systems for deploying learning-based policies on single-arm, dual-arm, and humanoid robots.
Applicants with experience or interest in robot learning, reinforcement learning, generative models, multimodal AI, PyTorch, ROS, or Isaac Lab are encouraged to apply.
We are seeking a highly motivated andtalented Research Assistant to join our research team in the exciting area of Edge AI system. The RA will work on cutting-edge projects focused on efficient, real-time AI reasoning on edge devices.
Key Research Projects The RA will contribute to one or more of the following research directions:
Edge Physical Embodied Intelligence: Deploying World-Action Models and Vision-Language-Action models on edge devices for real-time perception, planning, and action.
On-Device Multimodal Model: Optimizing multimodal AI models for real-time perception, reasoning, and interaction on resource-constrained edge platforms.
Memory-Centric Edge AI Systems: Designing hierarchical memory scheduling across SSD, DRAM, HBM, and device memory to support large-scale AI workloads on edge devices.
We are seeking a highly motivated andtalented Research Assistant to join our research team in the exciting area of Edge AI system. The RA will work on cutting-edge projects focused on efficient, real-time AI reasoning on edge devices.
Key Research Projects The RA will contribute to one or more of the following research directions:
Edge Physical Embodied Intelligence: Deploying World-Action Models and Vision-Language-Action models on edge devices for real-time perception, planning, and action.
On-Device Multimodal Model: Optimizing multimodal AI models for real-time perception, reasoning, and interaction on resource-constrained edge platforms.
Memory-Centric Edge AI Systems: Designing hierarchical memory scheduling across SSD, DRAM, HBM, and device memory to support large-scale AI workloads on edge devices.
