6 accepted papers
While LLM-based agents excel at individual tasks, effective collaboration with realistic human partners remains challenging. Most of the existing conversation-level collaborative studies lack grounded interaction and behavioral execution, motivating the need for cooperative game environments that en…
Offline black-box optimization aims to identify the optimal solution of a black-box objective function under the guidance of a surrogate model constructed solely from a pre-collected dataset. It is commonly used in industrial scenarios, which often involve constraints, i.e., constrained offline opti…
We explore cross-domain offline reinforcement learning (RL) where offline datasets from another domain can be accessed to facilitate policy learning. However, the underlying environments of the two datasets may have dynamics mismatches, incurring inferior performance when simply merging the data of…
It is vital to learn effective policies that can be transferred to different domains with dynamics discrepancies in reinforcement learning (RL). In this paper, we consider dynamics adaptation settings where there exists dynamics mismatch between the source domain and the target domain, and one can g…
"Neural Radiance Fields (NeRF) have achieved remarkable progress on dynamic scenes with deformable objects. Nonetheless, most previous works required multi-view inputs or long training time (several hours), making it hard to apply them for real-world scenarios. Recent works dedicated to addressing b…
We consider off-dynamics reinforcement learning (RL) where one needs to transfer policies across different domains with dynamics mismatch. Despite the focus on developing dynamics-aware algorithms, this field is hindered due to the lack of a standard benchmark. To bridge this gap, we introduce ODRL,…