NVSPolicy: Adaptive Novel-View Synthesis for Generalizable Language-Conditioned Policy Learning
This addresses robot manipulation in unstructured environments by improving generalization, though it appears incremental as it builds on existing generative models and policy learning methods.
The paper tackles the challenge of visual artifacts in generated images and inefficient multi-modal feature integration for robot manipulation by introducing NVSPolicy, which adaptively synthesizes novel views and uses a hierarchical policy network, achieving a 90.4% average success rate on CALVIN tasks.
Recent advances in deep generative models demonstrate unprecedented zero-shot generalization capabilities, offering great potential for robot manipulation in unstructured environments. Given a partial observation of a scene, deep generative models could generate the unseen regions and therefore provide more context, which enhances the capability of robots to generalize across unseen environments. However, due to the visual artifacts in generated images and inefficient integration of multi-modal features in policy learning, this direction remains an open challenge. We introduce NVSPolicy, a generalizable language-conditioned policy learning method that couples an adaptive novel-view synthesis module with a hierarchical policy network. Given an input image, NVSPolicy dynamically selects an informative viewpoint and synthesizes an adaptive novel-view image to enrich the visual context. To mitigate the impact of the imperfect synthesized images, we adopt a cycle-consistent VAE mechanism that disentangles the visual features into the semantic feature and the remaining feature. The two features are then fed into the hierarchical policy network respectively: the semantic feature informs the high-level meta-skill selection, and the remaining feature guides low-level action estimation. Moreover, we propose several practical mechanisms to make the proposed method efficient. Extensive experiments on CALVIN demonstrate the state-of-the-art performance of our method. Specifically, it achieves an average success rate of 90.4\% across all tasks, greatly outperforming the recent methods. Ablation studies confirm the significance of our adaptive novel-view synthesis paradigm. In addition, we evaluate NVSPolicy on a real-world robotic platform to demonstrate its practical applicability.