QuasiSim: Parameterized Quasi-Physical Simulators for Dexterous Manipulations Transfer (2404.07988v2)

Abstract: We explore the dexterous manipulation transfer problem by designing simulators. The task wishes to transfer human manipulations to dexterous robot hand simulations and is inherently difficult due to its intricate, highly-constrained, and discontinuous dynamics and the need to control a dexterous hand with a DoF to accurately replicate human manipulations. Previous approaches that optimize in high-fidelity black-box simulators or a modified one with relaxed constraints only demonstrate limited capabilities or are restricted by insufficient simulation fidelity. We introduce parameterized quasi-physical simulators and a physics curriculum to overcome these limitations. The key ideas are 1) balancing between fidelity and optimizability of the simulation via a curriculum of parameterized simulators, and 2) solving the problem in each of the simulators from the curriculum, with properties ranging from high task optimizability to high fidelity. We successfully enable a dexterous hand to track complex and diverse manipulations in high-fidelity simulated environments, boosting the success rate by 11\%+ from the best-performed baseline. The project website is available at https://meowuu7.github.io/QuasiSim/.

• The paper introduces parameterized quasi-physical simulators that balance simulation fidelity with computational tractability for dexterous tasks.
• It employs a physics curriculum that gradually increases simulation realism, enabling effective transfer of complex manipulation skills.
• Empirical results demonstrate significant improvements in success rates and task fidelity over conventional simulation methods.

Parameterized Quasi-Physical Simulators Enhance Dexterous Manipulation Transfer

Introduction to Quasi-Physical Simulators

The quest for efficient dexterous manipulation transfer—transferring human manipulation skills to robotic counterparts—encounters significant hurdles due to complex dynamic interactions, the need for precise control over high degrees of freedom, and the fidelity of simulation environments. Traditional approaches using high-fidelity or relaxed-constraint simulations have shown limited success in replicating complex, contact-rich manipulations. The proposed method introduces a novel framework leveraging parameterized quasi-physical simulators and a physics curriculum, aiming to balance simulation fidelity with optimizability. This approach significantly improves the success rate of dexterous manipulation transfers in simulated environments.

Key Contributions

The research presents several noteworthy contributions to the field of robotics and AI:

• A series of parameterized quasi-physical simulators allow for dynamic adjustment between simulation fidelity and optimizability, facilitating complex manipulation tasks typically hindered by conventional simulation constraints.
• The introduction of a quasi-physics curriculum systematically progresses from simpler to more realistic simulations, enabling effective skill transfer without sacrificing task complexity or manipulation accuracy.
• Empirical evidence demonstrates the method's superiority in transferring sophisticated manipulations, showcasing significant improvement over existing models, both qualitative and quantitative.

Parameterized Quasi-Physical Simulators

The core innovation lies in formulating simulators that dynamically balance physical realism with computational tractability. This balance is achieved through:

• Relaxed dynamics models, transitioning from rigid articulated bodies to parameterized point sets, providing a more malleable framework for modeling manipulations.
• Softened contact models, utilizing unconstrained parameterized spring-dampers to manage contact forces effectively, thus offering a smooth optimization landscape.

Curriculum Learning in Physics Simulation

Advancing from the established parameterized simulators, a curriculum of increasing realism guides the optimization process. This curriculum begins with highly optimized setups conducive to initial learning and incrementally adjusts parameters towards a physically accurate simulation. The methodology ensures continual progression and knowledge transfer between stages, culminating in successful manipulation in high-fidelity environments.

Methodological Framework

The approach encompasses several stages, beginning with initial manipulation transfer to a simplified simulation, followed by progressive refinement across a series of simulators with gradually increasing fidelity. The optimization leverages:

• Relaxed point set dynamics for initial manipulation learning, reducing the complexity of controlling high degrees of freedom.
• Curriculum-based optimization, gradually tightening simulation parameters to steer the learning process towards realistic physical interactions.
• Fine-tuning via residual physics networks, adding nuanced physical behaviors unmodeled in earlier stages, thereby aligning the simulation closely with real-world dynamics.

Experimental Validation

Extensive evaluations demonstrate the approach's effectiveness across diverse manipulations, including complex tool interactions and bimanual tasks. Compared to benchmarks, this method achieved notable improvements in success rates and task fidelity, substantiating its potential for advancing dexterous robotic manipulation.

Implications and Future Directions

The introduction of parameterized quasi-physical simulators opens new avenues for robotics research, particularly in how simulation environments can be structured for more effective learning. The ability to finely tune the balance between simulation fidelity and tractability presents compelling implications for both theoretical advancements and practical applications in robotic manipulation.

Looking ahead, this research suggests several promising trajectories, such as extending these methodologies to real-world robotics applications and exploring the potential of quasi-physical simulators in other areas of AI and machine learning. The adaptability and performance enhancements offered by this approach may well lay the groundwork for future breakthroughs in automated dexterous manipulation and beyond.