Nowhere to Go: Benchmarking Multi-robot Collaboration in Target Trapping Environment

Collaboration is one of the most important factors in multi-robot systems. Considering certain real-world applications and to further promote its development, we propose a new benchmark to evaluate multi-robot collaboration in Target Trapping Environment (T2E). In T2E, two kinds of robots (called captor robot and target robot) share the same space. The captors aim to catch the target collaboratively, while the target will try to escape from the trap. Both the trapping and escaping process can use the environment layout to help achieve the corresponding objective, which requires high collaboration between robots and the utilization of the environment. For the benchmark, we present and evaluate multiple learning-based baselines in T2E, and provide insights into regimes of multi-robot collaboration. We also make our benchmark publicly available and encourage researchers from related robotics disciplines to propose, evaluate, and compare their solutions in this benchmark. Our project is released at https://github.com/Dr-Xiaogaren/T2E.

HAM: Hierarchical Attention Model with High Performance for 3D Visual Grounding

This paper tackles an emerging and challenging vision-language task, namely 3D visual grounding on point clouds. Many recent works benefit from Transformer with the well-known attention mechanism, leading to a tremendous breakthrough for this task. However, we find that they realize the achievement by using various pre-training or multi-stage processing. To simplify the pipeline, we carefully investigate 3D visual grounding and summarize three fundamental problems about how to develop an end-to-end model with high performance for this task. To address these problems, we especially introduce a novel Hierarchical Attention Model (HAM), offering multi-granularity representation and efficient augmentation for both given texts and multi-modal visual inputs. Extensive experimental results demonstrate the superiority of our proposed HAM model. Specifically, HAM ranks first on the large-scale ScanRefer challenge, which outperforms all the existing methods by a significant margin. Codes will be released after acceptance.