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cs.RO2026
RoboQuest: Generalist Physical Agents that Search, Inspect and Test
Liu Renhang, Navonil Majumder, Tej Deep Pala +1
Recent advances in multimodal foundation models have made them capable generalist physical agents for a range of manipulation tasks. However, successful operation in an unfamiliar…
cs.RO2026
MemBodied: Recurrent Associative Memory for Vision-Language-Action Models
Tej Deep Pala, Navonil Majumder, Bryce Goh +4
Vision-Language-Action models provide a strong foundation for general-purpose robot control, yet a vast majority of policies do not preserve and leverage episode-level information…
cs.CL2026
GRAIL: Gradient-Reweighted Advantages for Reinforcement Learning with Verifiable Rewards
Tej Deep Pala, Vernon Toh, Soujanya Poria
Reinforcement learning with verifiable rewards (e.g. GRPO) is now a common way to improve mathematical reasoning in Large Language Models (LLMs). However, current methods usually b…