Hadi Tjandra
Papers
6
Total Citations
47
H-Index
5
About
Hadi Tjandra’s research lies at the intersection of developmental robotics, neural computation, and motor learning, with a focus on enabling robots to acquire complex behaviors through self-exploration rather than explicit programming. His work is anchored in the concept of “body babbling”—a process inspired by infant motor development—where robots generate random movements to learn the dynamics of their own bodies and tools. Tjandra’s major contributions include proposing neural network-based models for visual-motor integration, allowing a robot to learn drawing motions by associating images with motor sequences (13 citations). He also developed the “tool-body assimilation model,” which uses a multiple time-scales recurrent neural network (MTRNN) to let robots discover tool functions and motions without prior knowledge (12 citations). This work challenges traditional tool-use paradigms by enabling adaptive, dynamic motion learning in flexible-joint robots (8 citations). Tjandra’s research has been published in key robotics venues, demonstrating significant impact in advancing unsupervised, bio-inspired learning for autonomous systems. His models offer a pathway toward more adaptable and safe robots capable of learning in unstructured environments.
Research Focus
Key Achievements
Top Papers
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