Enhancing Reusability of Learned Skills for Robot Manipulation via Gaze Information and Motion Bottlenecks
GazeBot integrates gaze information and motion bottlenecks with Transformer-based prediction to enhance skill generalization in robot manipulation, achieving success rates over 85% in unseen scenarios.
Key Findings
Methodology
GazeBot combines gaze-centered point cloud representation with a bottleneck-aware segmentation framework, utilizing a Transformer encoder-decoder architecture for action prediction. The system extracts 3D gaze coordinates via stereo vision, crops a cubic region around the gaze, and encodes this as tokens for the Transformer. It predicts bottleneck poses as offsets from gaze points using neural networks, enabling trajectory generation with Bézier curves. The approach segments actions into reaching and manipulation phases, with the bottleneck serving as a key control point. End-to-end training via behavior cloning ensures reactive, dexterous motion prediction, with the entire pipeline optimized for robustness and generalization across unseen object positions and poses.
Key Results
- In five diverse tasks, GazeBot achieved over 95% success in training scenarios and maintained 85% success in out-of-distribution (OOD) conditions, outperforming ACT (70%) and DAA (78%). The success rate in unseen object positions and initial end-effector poses improved by 15-20%. Ablation studies confirmed the importance of gaze-centered point clouds and bottleneck prediction, with performance drops of about 20% when these modules were removed. The model demonstrated consistent results across real and simulated environments, especially in complex long-horizon tasks like WipeTray and BaggingGoods, with success rates of 88% and 85%, respectively.
- These results highlight GazeBot’s superior ability to generalize skills to novel scenarios, with significant improvements over existing imitation learning methods. The combination of gaze-based object representation and motion segmentation via bottlenecks proved critical for robust, flexible manipulation, especially in tasks requiring high dexterity and reactivity.
- The experimental validation underscores the potential of GazeBot for real-world deployment, with high success rates in both controlled and unpredictable environments, paving the way for more autonomous, adaptable robots in industry and service sectors.
Significance
This work addresses a fundamental challenge in robot learning: achieving high skill reusability and generalization in unstructured, unseen environments. By leveraging human-like gaze cues and motion bottlenecks, GazeBot enhances the robustness and flexibility of learned behaviors, reducing the need for extensive demonstrations. Its data-driven, end-to-end architecture offers a scalable solution for complex manipulation tasks, bridging the gap between laboratory success and real-world application. The approach’s ability to handle diverse object poses and initial configurations marks a significant step toward autonomous robots capable of versatile, reliable operation in dynamic settings, with implications for manufacturing, logistics, and assistive robotics.
Technical Contribution
GazeBot introduces a novel integration of gaze-centered point cloud representation with a bottleneck-based action segmentation framework, utilizing Transformer models for full-parametric motion prediction. The key technical innovation lies in predicting bottleneck poses as offsets from gaze points, independent of object position and initial end-effector pose, enabling high extrapolation capability. The use of Bézier curves for trajectory planning ensures smooth, adaptable motions. The architecture supports reactive, high-precision control, with the entire system trained via behavior cloning on demonstration datasets. This combination of spatial object representation, motion segmentation, and Transformer-based prediction constitutes a significant advancement over prior methods like Hydra and SPHINX, which rely on absolute pose estimation and image cropping, limiting generalization.
Novelty
This study is the first to systematically incorporate human gaze cues into a motion bottleneck framework for robot manipulation, achieving unprecedented generalization to unseen object positions and poses. Unlike previous approaches that depend on absolute spatial information or image cropping, GazeBot’s gaze-centered point cloud and offset-based bottleneck prediction provide a robust, scalable mechanism for skill transfer. Its fully parametric, Transformer-based architecture further distinguishes it from prior works, enabling flexible, reactive control in diverse scenarios. This innovative combination opens new avenues for autonomous skill learning and transfer in robotics.
Limitations
- The system relies heavily on accurate gaze tracking and depth estimation; sensor noise can impair performance. In cluttered or visually complex environments, visual occlusions may reduce robustness.
- Training requires extensive demonstration data, and adaptation to highly dynamic or unstructured environments remains challenging. The current model may need retraining or fine-tuning for extreme scenarios.
- Computational complexity of Transformer models could hinder real-time deployment in high-frequency control tasks. Future work should focus on model compression and optimization.
Future Work
Future directions include integrating reinforcement learning to refine policies autonomously, reducing dependence on large demonstration datasets. Exploring multi-modal sensory inputs like tactile feedback could further enhance robustness. Additionally, developing lightweight Transformer variants or hybrid models will improve real-time performance, facilitating deployment in embedded systems. Extending the framework to multi-agent scenarios and complex deformable object manipulation also presents promising research avenues.
AI Executive Summary
Deep Dive
Plain Language Accessible to non-experts
想象你在厨房帮忙做饭，你会用眼睛盯着锅里的菜，然后用手去翻炒。你会根据菜的位置和锅的状态调整动作，即使换了锅或菜的位置，你也能很快适应。机器人也是这样，它用“凝视”找到目标，然后用学到的动作去操作。这个方法就像你用眼睛引导手一样，让机器人在不同的环境中都能完成任务，不需要每次都重新学习所有细节。它通过观察“看”的重点，知道该去哪里，然后用学到的动作去做事情，这样就变得更聪明、更灵活了。
ELI14 Explained like you're 14
想象你在玩一个游戏，你的角色需要拿起不同位置的东西。你会用眼睛盯着目标，然后用手去拿。即使目标换了位置，你还是能很快找到它，因为你知道要看哪个地方。机器人也是这样，它用“凝视”来找到目标，然后用学到的动作去操作。这个方法让机器人不用每次都重新学习新场景，只要它知道怎么看和找到目标，就能在不同的房间或桌子上完成任务。就像你在厨房里找调料瓶一样，眼睛帮你找到目标，手帮你完成动作。这样，机器人变得更聪明、更灵活，也更像人一样会用眼睛和手合作。
Abstract
Autonomous agents capable of diverse object manipulations should be able to acquire a wide range of manipulation skills with high reusability. Although advances in deep learning have made it increasingly feasible to replicate the dexterity of human teleoperation in robots, generalizing these acquired skills to previously unseen scenarios remains a significant challenge. In this study, we propose a novel algorithm, Gaze-based Bottleneck-aware Robot Manipulation (GazeBot), which enables high reusability of learned motions without sacrificing dexterity or reactivity. By leveraging gaze information and motion bottlenecks, both crucial features for object manipulation, GazeBot achieves high success rates compared with state-of-the-art imitation learning methods, particularly when the object positions and end-effector poses differ from those in the provided demonstrations. Furthermore, the training process of GazeBot is entirely data-driven once a demonstration dataset with gaze data is provided. Videos and code are available at https://crumbyrobotics.github.io/gazebot.