Seer - an end-to-end operation model developed by Shanghai AI Lab in collaboration with Peking University and other institutions.
Seer is an end-to-end operation model jointly developed by the Shanghai AI Lab, the School of Computer Science and Technology at Peking University, and the School of Software and Microelectronics at Peking University, achieving a high degree of coordination between robot vision prediction and action execution...
What is Seer?
Seer is an end-to-end operation model jointly developed by the Shanghai AI Lab, the School of Computer Science and Technology at Peking University, and the School of Software and Microelectronics at Peking University. It achieves a high degree of coordination between robot vision prediction and action execution. The model combines historical information and target signals (such as verbal commands) to predict future states and generates action signals using an inverse dynamics model. Based on a Transformer architecture, Seer processes multimodal input data, effectively fusing visual, verbal, and robot body signals. In real-world robot tasks, Seer's operation success rate is 43% higher than the current Sota model, and it demonstrates excellent generalization ability in various complex scenarios. In the CALVIN ABC-D Benchmark, a benchmark for control algorithms, Seer achieves an average task completion length of 4.28, leading similar models overall.
Seer's main functions
- Action predictionBased on the current visual state and the target, predict the appropriate robot action. Estimate the intermediate action sequence required to achieve the target based on the inverse dynamics model.
- Visual predictionSeer features conditional visual prediction capabilities, enabling it to predict RGB images within a certain time frame. This allows the robot to "foresee" future visual states, allowing for better planning and adjustment of its actions.
- Multimodal fusionThis technology integrates information from multiple modalities, including vision, language, and robot state, to achieve the understanding and execution of complex tasks. Based on a multimodal encoder, it integrates features from different modalities, providing comprehensive contextual information for action and visual prediction.
- Generalization abilityAfter being pre-trained on a large-scale robotic dataset, Seer demonstrates strong generalization ability, maintaining stable performance even in unseen scenes, new objects, different lighting conditions, and when facing high-intensity interference.
- Data efficiencySeer learns rich prior knowledge from a large amount of data during the pre-training stage, so only a small amount of fine-tuning data is needed in downstream tasks to achieve good performance, reducing the cost of data collection and labeling.
Seer's technical principles
- End-to-end architectureBased on an end-to-end architecture, visual prediction and inverse dynamics prediction are tightly integrated. During training, the visual prediction module and the inverse dynamics module work together to optimize, allowing the model to fully utilize visual and motion information to achieve more accurate motion prediction.
- Transformer architectureIt uses the Transformer architecture to process visual state and action information. The Transformer can capture complex dependencies in visual and action sequences, providing the model with powerful feature extraction and representation capabilities.
- First-look tokens and action tokensSeer introduces a foresight token and an action token. The foresight token predicts the future RGB image, while the action token estimates the intermediate action between the current observation and the predicted future observation. The two tokens are fused based on a multimodal encoder with the input RGB image, robot state, and language token, using a unidirectional attention mask to achieve deep information integration.
- One-way attention maskSeer employs a unique unidirectional attention mask that allows action tokens to fully integrate past and future prediction information. This helps the model achieve deeper information fusion in multi-layer networks, improving the accuracy and robustness of action prediction.
- Large-scale pre-training and fine-tuningSeer first pre-trains on a large-scale robotics dataset (such as DROID) to learn rich visual and action prior knowledge. In downstream tasks, the model is adjusted based on a small amount of fine-tuning data to adapt to specific task scenarios and objectives.
Seer's project address
- Project official website:https://nimolty.github.io/Seer
- GitHub repository:https://github.com/OpenRobotLab/Seer
- arXiv technical paper:https://arxiv.org/pdf/2412.15109
Seer's application scenarios
- Industrial AutomationIt guides robots to accurately install automotive parts, improving assembly efficiency and quality.
- service robotsThis helps service robots deliver items accurately to guest rooms as needed, enhancing the customer experience.
- HealthcareAs the core of virtual surgical robots, it assists medical students in learning and practicing surgical skills.
- Logistics and WarehousingAutomated sorting systems quickly and accurately sort packages into designated channels, improving sorting efficiency.
- Education industryAs a teaching case, it helps students gain a deeper understanding of advanced techniques and algorithms in robot programming.