GroupACT: Functional-Group Coordination for Robot Action Generation

Large-scale robot models learn diverse motion patterns from data collected across embodiments. However, robot embodiments, particularly mobile manipulation and multi-arm platforms, comprise heterogeneous joints with distinct functional roles and motion characteristics. These differences shape the temporal and spatial structure of joint trajectories. Incorporating this structure as a physical prior can guide the learning of coordinated actions across heterogeneous control variables. In this work, we assign each joint a group label based on its functional role and motion characteristics and organize joints into action groups. Using the articulated structure of each embodiment, we further construct a joint-group graph that explicitly represents the relationships between joints and their functional groups. We then introduce GroupACT, a flow-matching policy that incorporates these structural priors into its action expert through group-mediated block and group-query tokens. The group-mediated block is designed for integration into Transformer-based action experts. Experiments on mobile manipulation in simulation and bimanual manipulation on a physical robot show that GroupACT outperforms the evaluated baselines on multiple tasks. These results support the effectiveness of functional-group priors for learning heterogeneous actions across the evaluated embodiments and tasks.