Twisting Lids Off with Two Hands (2403.02338v2)

Abstract: Manipulating objects with two multi-fingered hands has been a long-standing challenge in robotics, due to the contact-rich nature of many manipulation tasks and the complexity inherent in coordinating a high-dimensional bimanual system. In this work, we share novel insights into physical modeling, real-time perception, and reward design that enable policies trained in simulation using deep reinforcement learning (RL) to be effectively and efficiently transferred to the real world. Specifically, we consider the problem of twisting lids of various bottle-like objects with two hands, demonstrating policies with generalization capabilities across a diverse set of unseen objects as well as dynamic and dexterous behaviors. To the best of our knowledge, this is the first sim-to-real RL system that enables such capabilities on bimanual multi-fingered hands.

• The paper introduces a simulation-to-real framework using deep reinforcement learning to achieve effective bimanual lid twisting.
• It advances physical modeling and a minimalist perception system to balance fidelity and computational speed in robotic manipulation.
• Controlled experiments show policy robustness across diverse objects, underscoring the approach's potential for complex manipulation tasks.

Advancing Bimanual Manipulation: Twisting Lids with Robotic Hands

Introduction to Bimanual Manipulation Challenges

Bimanual manipulation, the task of using two robotic hands to interact with objects, stands as a complex challenge that bridges fundamental robotics, dexterity, and coordination. This research specifically explores twisting or removing lids from bottle-like objects, a task that not only mirrors a common human activity but also presents a significant trial for robotic systems due to the intricate coordination and force application required.

Research Objectives and Approach

The primary aim of this paper is to demonstrate that policies trained in simulation via deep reinforcement learning (RL) can be successfully transferred to real-world bimanual manipulation tasks, specifically focusing on twisting lids off bottles. A key aspect of this work is the adoption and advancement of sim-to-real techniques, which entail training in a simulated environment before deploying in the real world. This approach is underpinned by novel contributions in physical modeling for simulation, a minimal yet sufficient perception system, and a strategic reward design facilitating the training of sophisticated manipulation policies.

Innovations in Physical Modeling and Perception

The paper introduces several innovations in physical modeling and perception for enhanced sim-to-real transfer:

• Physical Modeling: It presents a brake-based design for simulating the interaction between a bottle and its lid, efficiently balancing simulation fidelity and computational speed. This allows for the realistic reproduction of friction and contact dynamics critical for lid-twisting tasks.
• Perception: Contrary to initial assumptions that a detailed perception model is necessary, the research reveals that a two-point sparse representation of the object, combined with domain randomization for training, suffices for the task. This insight significantly simplifies the perception requirements without sacrificing performance.

Reward Design and Policy Learning

The work explores the design of a multi-component reward function that encourages both the natural positioning of robot fingers around the object and the execution of twisting motion. This reward design is pivotal in guiding the RL agent towards successful task completion. Through a series of controlled experiments in simulation and real-world settings, the paper validates the effectiveness of the proposed reward structure and the overall learning framework.

Empirical Validation and Results

Controlled experiments underscore the efficacy of the developed models and methods. The successful policies demonstrate robustness across various test objects differing in physical properties, showcasing the capability to generalize learned skills to new, unseen objects. Moreover, the real-world deployment of these policies further validates their practical applicability and the potential of sim-to-real approaches for complex manipulation tasks.

Implications and Future Directions

This research makes significant strides in the field of robotic bimanual manipulation, especially in handling complex, contact-rich tasks like lid twisting. The demonstrated success of simulation-trained policies in real-world applications not only strengthens confidence in sim-to-real methods but also opens avenues for exploring other sophisticated manipulation tasks using similar frameworks. Future research could extend these techniques to even more dynamic and intricate manipulations, potentially incorporating advanced perception models and exploring the limits of policy generalization across diverse tasks.

Conclusion

In sum, this work presents a comprehensive approach to addressing the challenge of bimanual manipulation with a focus on twisting lids off bottles. Through meticulous research and experimentation, it contributes novel insights into physical modeling, perception, and reward design for deep reinforcement learning, significantly advancing the capabilities of robotic systems in performing tasks that require finesse, dexterity, and coordination.