Krishna Murthy, a postdoc at MIT, researches computational world models to enable robots to understand and operate effectively in the physical world. His work focuses on differentiable computing approaches for spatial perception and interfaces large image, language, and audio models with 3D scenes. Murthy envisions structured world models working with scaling-based approaches to create versatile robot perception and planning algorithms. Why it matters: This research could significantly advance robotics by enabling more sophisticated perception, reasoning, and action capabilities in embodied agents.
Gregory Chirikjian presented an overview of research on robot navigation in unstructured environments, using computer vision, sensor tech, ML, and motion planning. The methods use multi-modal observations from RGB cameras, 3D LiDAR, and robot odometry for scene perception, along with deep RL for planning. These methods have been integrated with wheeled, home, and legged robots and tested in crowded indoor scenes, home environments, and dense outdoor terrains. Why it matters: This research pushes the boundaries of robotics in complex environments, paving the way for more versatile and autonomous robots in the Middle East.
Dezhen Song from Texas A&M University presented a talk on Co-Modality Active sensing and Perception (C-MAP) for robotics, covering sensor fusion for autonomous vehicles, augmented reality, and remote environmental monitoring. The talk highlighted lessons learned in sensor fusion using autonomous motorcycles and NASA Robonaut as examples. Recent works in robotic remote environment monitoring, especially focused on subsurface surface void and pipeline mapping were discussed. Why it matters: This research explores sensor fusion techniques to enhance robot perception, which could improve the robustness and capabilities of autonomous systems developed and deployed in the Middle East, particularly in challenging environments.
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.
Lorenzo Jamone from Queen Mary University of London presented on cognitive robotics, focusing on tactile exploration and manipulation by robots. The talk covered combining biology, engineering, and AI for advanced robotic systems. Jamone directs the CRISP group and has over 100 publications in cognitive robotics. Why it matters: This highlights the ongoing research into more sophisticated robotic systems that can interact with complex environments, an area crucial for future applications in manufacturing and human-robot collaboration in the GCC.
Cyrill Stachniss from the University of Bonn presented recent work on agricultural robotics and self-driving cars. The talk covered autonomous field robots and their ability to perceive, model, and predict future developments in complex farming environments. The presentation also included developments in supervised and unsupervised learning for autonomous car perception systems. Why it matters: This highlights the growing interest in robotics research at MBZUAI and the potential for AI to transform key sectors in the GCC region like agriculture and transportation.
Gregory Chirikjian presented an overview talk on applying probability, harmonic analysis, and geometry to robotics, emphasizing the need for robots to function beyond traditional industrial programming. He discussed a new approach where robots define affordances of objects, using simulation to 'imagine' object use and enabling reasoning about novel objects. Probabilistic methods on Lie-groups, initially developed for mobile robot state estimation, are now adapted for one-shot learning of affordances, with plans to integrate large language models. Why it matters: This research direction aims to enhance robot intelligence and adaptability, crucial for service robots in dynamic environments and aligning with broader goals of advanced AI integration in robotics.
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.