MorphoGuard: A Morphology-Based Whole-Body Interactive Motion Controller

Abstract

Whole-body control (WBC) has demonstrated significant advantages in complex interactive movements of high-dimensional robotic systems. However, when a robot is required to handle dynamic multi-contact combinations along a single kinematic chain-such as pushing open a door with its elbow while grasping an object-it faces major obstacles in terms of complex contact representation and joint configuration coupling. To address this, we propose a new control approach that explicitly manages arbitrary contact combinations, aiming to endow robots with whole-body interactive capabilities. We develop a morphology-constrained WBC network (MorphoGuard)-which is trained on a self-constructed dual-arm physical and simulation platform. A series of model recommendation experiments are designed to systematically investigate the impact of backbone architecture, fusion strategy, and model scale on network performance. To evaluate the control performance, we adopt a multi-object interaction task as the benchmark, requiring the model to simultaneously manipulate multiple target objects to specified positions. Experimental results show that the proposed method achieves a contact point management error of approximately 1 cm, demonstrating its effectiveness in whole-body interactive control.

Figures (7)

• Figure 1: Schematic of morphology-based whole-body motion control (MorphoGuard). (A) An example of a robot facing complex contact combinations in an unstructured environment. (B) MorphoGuard is tasked with learning a mapping from the entity morphology in the workspace to the configuration space. (C) MorphoGuard takes as input MPs that discretely represent the robot morphology and predicts the corresponding joint motion commands.
• Figure 2: An overview of the MorphoGuard framework. The model consists of a MPs encoding module, an fusion module, and a joint command decoding module. The morphology encoding module encodes the current morphology and the target morphology into feature representations. The information fusion module integrates the encoded features to capture the relationship between the current and target morphologies. Finally, the joint command decoding module predicts the joint commands based on the fused features.
• Figure 3: Simulation and physical platform used for data collection.
• Figure 4: Training and validation loss curves for different backbone architectures. The MLP backbone demonstrates the lowest validation loss, indicating superior generalization performance compared to CNN, Transformer, and GNN backbones.
• Figure 5: