Diffusion Policy for Force- and Vision-Guided Robotic Liquid Pouring Task

Sammanfattning

Liquid pouring is a critical, high-precision task in laboratory automation and pharmaceutical manufacturing. Existing imitation learning methods struggle with the high demands for accuracy and robustness in contact-rich pouring tasks. This thesis presents an extension of the diffusion policy framework by integrating force feedback for a dual-arm robot pouring approach. Unlike traditional methods, diffusion-based policy effectively captures the multi-modal action distributions inherent in pouring tasks, where diverse trajectories may lead to successful outcomes. Additionally, a diffusion policy generates temporal action chunks rather than point-wise outputs, which ensures smooth, continuous control suitable for temporally extended pouring motions. To complement these capabilities with physical awareness, we augment the observation space by directly integrating joint torque signals as proprioceptive inputs together with visual observations. This multimodal observation design allows the policy to condition action generation on visual information, robot joint states, and raw joint torque signals, which provide force-related cues about load changes and physical interaction during pouring. For safety during data collection, rice is used as a proxy medium instead of real liquids. The policy, trained on 150 human demonstrations, was evaluated over 60 trials (20 per configuration) to compare the single-view baseline, multi-view baseline, and force-integrated multi-view policy. A trial is recorded as successful if the robot precisely transfers rice into the target container without missing and both arms return to designated positions. Results show that the force-integrated multi-view policy achieved the highest overall success rate and improved task progression compared with policies without force-feedback input. However, the system is evaluated using rice as a granular proxy medium, and its generalization to real liquid pouring with different fluid properties remains an objective for future work.