ArtiGrasp: Physically Plausible Synthesis of Bi-Manual Dexterous Grasping and Articulation

Abstract: We present ArtiGrasp, a novel method to synthesize bi-manual hand-object interactions that include grasping and articulation. This task is challenging due to the diversity of the global wrist motions and the precise finger control that are necessary to articulate objects. ArtiGrasp leverages reinforcement learning and physics simulations to train a policy that controls the global and local hand pose. Our framework unifies grasping and articulation within a single policy guided by a single hand pose reference. Moreover, to facilitate the training of the precise finger control required for articulation, we present a learning curriculum with increasing difficulty. It starts with single-hand manipulation of stationary objects and continues with multi-agent training including both hands and non-stationary objects. To evaluate our method, we introduce Dynamic Object Grasping and Articulation, a task that involves bringing an object into a target articulated pose. This task requires grasping, relocation, and articulation. We show our method's efficacy towards this task. We further demonstrate that our method can generate motions with noisy hand-object pose estimates from an off-the-shelf image-based regressor.

• The paper presents a unified reinforcement learning framework that integrates grasping and articulation into a single, efficient policy within a physics-based simulation.
• It employs a progressive curriculum learning strategy to master complex finger and wrist control for precise bi-manual interactions.
• ArtiGrasp outperforms baseline methods by up to five times in dynamic tasks, demonstrating robust performance under noisy hand-object pose estimates.

Analysis of "Physically Plausible Synthesis of Bi-Manual Grasping and Articulation"

This paper introduces "ArtiGrasp," a novel method for synthesizing bi-manual hand-object interactions that encompass grasping and articulation. This research addresses the challenges associated with the generation of realistic and physically plausible motion sequences involving complex bi-manual interactions, which are critical in various domains such as robotics, animation, and virtual reality.

Methodology

The proposed method leverages reinforcement learning (RL) within a physics-based simulation environment. The core of the framework is a policy that controls both the global and local hand poses, integrating grasping and articulation into a unified system. A single hand pose reference guides the policy, making the system efficient with minimal data requirements.

The authors implement a progressive learning curriculum to handle the complexity of finger control needed for successful articulation. Initially, training focuses on single-hand manipulation with stationary objects before advancing to scenarios involving both hands and non-stationary objects. This step-wise training approach addresses the challenges of diverse wrist motions and precise finger articulations required by bi-manual tasks.

Evaluation and Results

To evaluate the effectiveness of ArtiGrasp, the authors introduce the Dynamic Object Grasping and Articulation task, which demands transitioning an object to a target articulated pose through grasping and relocation. The results, as indicated in the paper, show that ArtiGrasp outperforms existing methods, including adapted versions of related work like D-Grasp, particularly in terms of task success rates and handling noise in hand-object pose estimates. The method reports performance gains of up to five times over baselines in dynamic tasks, establishing its robustness and efficacy in generating plausible bi-manual interactions.

Contributions and Implications

The contributions of this research are multi-faceted:

1. Unified Policy Framework: The method successfully integrates grasping and articulation in a single reinforcement learning framework, thus simplifying the data requirements and enhancing computational efficiency.
2. Physics-based Simulation: By employing a physics-based environment, the method ensures physically plausible motion sequences that align with real-world interactions.
3. Curriculum Learning: The innovative curriculum learning strategy effectively breaks down the complexity of bi-manual interaction tasks, facilitating the gradual acquisition of precise control skills.
4. Scalability and Generalization: Demonstrated scalability across diverse objects without the need for task-specific retraining signifies the potential for generalization to unseen contexts, albeit with some limitations noted in the proof-of-concept evaluation.

Future Directions

The paper opens avenues for further investigation into enhancing the naturalness of synthesized hand poses, perhaps through integrating biomechanical constraints or data-driven hand pose priors. Moreover, the authors highlight the potential for extending the framework to handle more complex scenarios, possibly involving multi-object interactions or more sophisticated planning and decision-making processes.

Conclusion

In summary, this work presents a substantial advancement in the synthesis of bi-manual hand-object interactions, with implications spanning multiple applications in interactive and automated systems. The method's robust reinforcement learning framework combined with innovative training protocols sets a new benchmark for the generation of physically plausible, realistic motion sequences in computer simulations.