Gripper Keypose and Object Pointflow as Interfaces for Bimanual Robotic Manipulation (2504.17784v1)

Abstract: Bimanual manipulation is a challenging yet crucial robotic capability, demanding precise spatial localization and versatile motion trajectories, which pose significant challenges to existing approaches. Existing approaches fall into two categories: keyframe-based strategies, which predict gripper poses in keyframes and execute them via motion planners, and continuous control methods, which estimate actions sequentially at each timestep. The keyframe-based method lacks inter-frame supervision, struggling to perform consistently or execute curved motions, while the continuous method suffers from weaker spatial perception. To address these issues, this paper introduces an end-to-end framework PPI (keyPose and Pointflow Interface), which integrates the prediction of target gripper poses and object pointflow with the continuous actions estimation. These interfaces enable the model to effectively attend to the target manipulation area, while the overall framework guides diverse and collision-free trajectories. By combining interface predictions with continuous actions estimation, PPI demonstrates superior performance in diverse bimanual manipulation tasks, providing enhanced spatial localization and satisfying flexibility in handling movement restrictions. In extensive evaluations, PPI significantly outperforms prior methods in both simulated and real-world experiments, achieving state-of-the-art performance with a +16.1% improvement on the RLBench2 simulation benchmark and an average of +27.5% gain across four challenging real-world tasks. Notably, PPI exhibits strong stability, high precision, and remarkable generalization capabilities in real-world scenarios. Project page: https://yuyinyang3y.github.io/PPI/

Gripper Keypose and Object Pointflow as Interfaces for Bimanual Robotic Manipulation

The paper "Gripper Keypose and Object Pointflow as Interfaces for Bimanual Robotic Manipulation" introduces an innovative approach to robotic manipulation, particularly concerning bimanual tasks that require both precise spatial localization and versatile motion trajectories. The authors propose an end-to-end framework named PPI (keyPose and Pointflow Interface), which integrates target gripper poses and object pointflow predictions to facilitate continuous action estimation for enhanced robotic performance.

Overview and Methodology

Bimanual robotic manipulation poses significant challenges in terms of coordination and spatial awareness. Existing techniques fall into two main categories: keyframe-based strategies and continuous control methods. Keyframe-based strategies focus on predicting actions for specific reference frames and executing them through motion planners. While effective for spatial localization, these strategies often struggle with tasks involving curved trajectories or intricate motion constraints. Continuous control methods estimate actions for each timestep, providing greater flexibility in motion but often leading to weaker spatial perception due to dense supervision and potential overfitting to seen trajectories.

To address these limitations, the authors present PPI, a framework that employs two key interfaces—target gripper poses at keyframes and object pointflow. By predicting continuous actions conditioned on these interfaces, PPI effectively balances spatial awareness with task flexibility. This integration allows for detailed modeling of interactions between the robot and the object, enhancing the execution of diverse and collision-free trajectories. PPI employs a diffusion transformer to process the interfaces, ensuring progressive inference of actions with unidirectional attention, thus leveraging spatial features comprehensively.

Numerical Results and Evaluation

The authors conduct extensive evaluations of PPI on both simulated and real-world scenarios. In simulated environments, the model demonstrates a 16.1% improvement in success rate on the RLBench2 simulation benchmark, outperforming state-of-the-art baselines across seven diverse tasks. The real-world experiments validate the model's robustness and effectiveness, achieving an average improvement of 27.5% across four complex tasks that demand high spatial precision and motion control.

The results strongly emphasize PPI's capability to maintain stability and precision in real-world scenarios, showcasing remarkable generalization capabilities even under varied conditions such as lighting changes and introduction of object interference. The interfaces, particularly the object pointflow, ensure that the model focuses on key object regions during manipulation, rendering it less susceptible to distractions and enhancing its adaptability to unseen objects.

Implications and Future Directions

The PPI framework exemplifies a significant leap in robotic manipulation by integrating spatial awareness with flexible task execution. The highlighted interfaces effectively bridge the gap between perception and action planning, a critical aspect that enhances the overall efficacy of manipulation tasks. Practically, this approach offers potential improvements in industrial and service robotics where complex bimanual tasks are prevalent.

From a theoretical perspective, the integration of diffusion models and attention mechanisms in manipulation tasks opens new avenues for research in understanding and optimizing robot-object interactions. Future developments could focus on reducing computational costs and exploring cross-embodiment evaluations to assess the generalizability of these interfaces across different robotic platforms.

In conclusion, the paper presents a compelling case for the use of gripper keypose and object pointflow as crucial interfaces in robotic manipulation. The strong numerical results corroborate the effectiveness of the approach, making it a valuable contribution to the domain of robotics.