Adithya Bellathur
Papers
1
Total Citations
282
H-Index
1
About
Adithya Bellathur is a leading researcher in robotics and artificial intelligence, with a primary focus on meta-reinforcement learning and multi-task learning. His most influential work, "Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning" (2019), has garnered over 280 citations and fundamentally reshaped how the field evaluates generalization in robotic skill acquisition. By exposing the limitations of narrow task distributions in existing benchmarks, Bellathur introduced a diverse suite of 50 distinct manipulation tasks that rigorously tests an agent’s ability to learn new skills from prior experience. This contribution has become a standard evaluation tool, driving progress toward more practical, adaptable robots. Beyond benchmarking, his research explores algorithms that enable faster learning through structured exploration and shared representations, directly addressing the challenge of sample efficiency in real-world robotics. Bellathur’s work bridges the gap between theoretical meta-learning and deployable robotic systems, making him a key figure in advancing autonomous agents that can truly learn how to learn.
Research Focus
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Top Papers
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