Thomas Stewart
Papers
1
Total Citations
2
H-Index
1
About
Thomas Stewart is a roboticist whose research focuses on advancing dexterous manipulation, particularly through learning-based grasp generation for underactuated robotic hands. His key contributions lie in bridging the gap between traditional precision grasping—where each finger makes a single contact point—and more robust power grasping, which envelops objects with multiple contact points for greater stability. In his notable 2024 work, "Gravity-aware Grasp Generation with Implicit Grasp Mode Selection for Underactuated Hands," Stewart introduced a novel data generation and learning pipeline that enables robots to autonomously select between precision and power grasps based on object geometry and task demands. This approach, which accounts for gravitational effects, significantly improves grasp reliability in real-world scenarios. Though early in his career, his work has already garnered attention within the manipulation community, with his top-cited paper accumulating 2 citations. Stewart’s research promises to enhance the versatility of underactuated hands, making them more practical for applications in manufacturing, assistive robotics, and household automation. His innovative integration of grasp mode selection with gravity awareness marks him as an emerging leader in robotic manipulation.
Research Focus
Key Achievements
Top Papers
- 1