MBZUAI has launched a full robotics program focusing on robot learning, humanoid robotics, human-computer interaction, and sensory awareness. The program includes research and teaching labs for experimentation and aims to address challenges in agriculture, logistics, and healthcare. The robotics department, founded in 2023, welcomed 18 students in August and is expected to grow to more than 15 faculty members over the next two years. Why it matters: This program signifies a major investment in AI for physical systems in the UAE, potentially leading to innovations across sectors and strengthening MBZUAI's position as a leading AI research institution.
MBZUAI has established new robotics and computer science departments, launching master’s and Ph.D. programs in each discipline. The new departments will complement MBZUAI’s existing CV, ML, and NLP departments. The robotics department will focus on robot learning and algorithms, while computer science will focus on foundational technologies with an emphasis on entrepreneurship and sustainability. Why it matters: This expansion strengthens the UAE's AI ecosystem and positions MBZUAI as a hub for AI research and innovation, addressing the growing global demand for AI talent.
MBZUAI researchers have developed a new action tokenization method called LipVQ-VAE to improve in-context robot learning. LipVQ-VAE combines VQ-VAE with a Lipschitz constraint to generate smoother robotic motions, addressing limitations of traditional methods. The technique was tested on simulated and real robots, showing improved performance in imitation learning. Why it matters: This research advances robot learning by enabling more fluid and successful robot actions through improved action representation, drawing inspiration from NLP techniques.
MBZUAI researchers have developed a "divide-and-conquer" technique to improve learning from demonstration in robotics. The approach breaks down complex dynamical systems into independently solvable subsystems, modeled as linear parameter-varying systems. This method aims to simplify computations while maintaining stability and accurately capturing joint interactions for robots in complex environments. Why it matters: The research addresses a key challenge in robotics, potentially enabling more efficient and safer robot learning from human demonstrations.
Song Chaoyang from the Southern University of Science and Technology (SUSTech) presented research on Vision-Based Tactile Sensing (VBTS) for robot learning, combining soft robotic design with learning algorithms to achieve state-of-the-art performance in tactile perception. Their VBTS solution demonstrates robustness up to 1 million test cycles and enables multi-modal outputs from a single, vision-based input, facilitating applications such as amphibious tactile grasping and industrial welding. The talk also highlighted the DeepClaw system for capturing human demonstration actions, aiming for a universal interaction interface. Why it matters: This research advances embodied intelligence by improving robot dexterity and adaptability through enhanced tactile sensing, which is crucial for complex manipulation tasks in various sectors such as manufacturing and healthcare within the region.
Mingyu Ding from UC Berkeley presented research on endowing robots with human-like commonsense and physical reasoning capabilities. The talk covered multimodal commonsense reasoning integrating vision, world models, and language-based task planners. It also discussed physical reasoning approaches for robots to infer dynamics and physical properties of objects. Why it matters: Enhancing robots with these capabilities can improve their ability to generalize across everyday tasks, leading to greater social benefits and impact.