Beyond Joint Angles: Learning Spatial Robot Arm Structure for Collision-Free Policy

Collision avoidance is a key challenge in bimanual manipulation. We argue that the essence of this problem is that proprioceptive representations such as joint angles lack information about the manipulator’s spatial structure. Based on this insight, we propose RoboCloud, a structural-prior descriptor that models both the geometry of entire manipulator and the topology of link connection, providing both spatial structure and kinematics for predicting potential surface contact either between the manipulators or between a manipulator and an observed object. Building on this representation, we introduce SPACE Policy, an imitation learning framework that uses Gated Bimanual Fusion to perceive spatial relationships between manipulators for self-collision avoidance and Gated Robot-Prior Fusion to perceive object–robot spatial relationships for environmental collision avoidance. SPACE Policy achieves an average success rate of 69.1% across 31 tasks on the RoboTwin 2.0 benchmark with a 11.9% improvement, and an average success rate of 83% across 7 tasks on the RLBench2 benchmark with a 2.2% improvement, and it demonstrates strong collision avoidance capability on real-world tasks. Our code will be released publicly to facilitate reproducibility and further research.