4 papers
Let's Think in Two Steps: Mitigating Agreement Bias in MLLMs with Self-Grounded Verification
Moises Andrade, Joonhyuk Cha, Brandon Ho +3
Verifiers--functions assigning rewards to agent behavior--have been key to AI progress in math, code, and games. However, extending gains to domains without clear-cut success crite…
Memo: Training Memory-Efficient Embodied Agents with Reinforcement Learning
Gunshi Gupta, Karmesh Yadav, Zsolt Kira +2
To enable embodied agents to operate effectively over extended timeframes, it is crucial to develop models that form and access memories to stay contextualized in their environment…
FindingDory: A Benchmark to Evaluate Memory in Embodied Agents
Karmesh Yadav, Yusuf Ali, Gunshi Gupta +2
Large vision-language models have recently demonstrated impressive performance in planning and control tasks, driving interest in their application to real-world robotics. However,…
ReLIC: A Recipe for 64k Steps of In-Context Reinforcement Learning for Embodied AI
Ahmad Elawady, Gunjan Chhablani, Ram Ramrakhya +4
Intelligent embodied agents need to quickly adapt to new scenarios by integrating long histories of experience into decision-making. For instance, a robot in an unfamiliar house in…